1. science
2. physics
3. optics

SLOVAK UNIVERSITY OF TECHNOLOGY IN BRATISLAVA
FACULTY OF CIVIL ENGINEERING
Reg. No.: SvF-5330- 67547
MONITORING LANDSCAPE CHANGES
USING SATELLITE RADAR IMAGERY
MASTER'S THESIS
2015
Bc. Jakub Vanko
SLOVAK UNIVERSITY OF TECHNOLOGY IN BRATISLAVA
FACULTY OF CIVIL ENGINEERING
Reg. No.: SvF-5330- 67547
MONITORING LANDSCAPE CHANGES
USING SATELLITE RADAR IMAGERY
MASTER'S THESIS
Study programme:
Study field number:
Study field:
Training workplace:
Thesis supervisor:
Consultant :
Bratislava 2015
Geodesy and Cartography
3636
5.1.3. Geodesy and Cartography
Department of Theoretical Geodesy
Ing. Juraj Papčo, PhD.
Doc. RNDr. Zuzana Krivá, PhD.
Bc. Jakub Vanko
Affidavit
I hereby declare that this master's thesis was written by myself on the basis of
theoretical and practical knowledge obtained during my studies. No further sources were
used than those explicitly stated. All citations quoted from publications in this thesis are
labelled as such.
Bratislava 21. May 2015
.....................
Bc. Jakub Vanko
Acknowledgements
TerraSAR-X radar imagery for this master's thesis was kindly provided by the
German Aerospace Centre (DLR) within the project ID LAN1583: Object Recognition
Based on High-Resolution Radar Imagery and the project LAN2833: Utilization of the
High Spatio-temporal Resolution of TerraSAR-X Observations for Recent Ground
Deformation Monitoring and SAR Image Processing. Sentinel-1 dataset was provided by
the Sentinel-1 Scientific Data Hub of the European Space Agency.
I would like to thank my supervisor and now a fellow colleague from the insar.sk
team Ing. Juraj Papčo, PhD. for introducing me to the beautiful science of SAR, for
providing me hints and new insights and steering me on the right track to completing this
master's thesis.
I also appreciate the help of Doc. RNDr. Zuzana Krivá, PhD. from the Department
of Mathematics and Descriptive Geometry SvF STU for answering my beginner questions
about image processing, hours of consultations and also for providing the Perona-Malik
equation based filter for denoising SAR images which were used in my thesis.
I am also very thankful to Ing. Matúš Bakoň for accepting me to the insar.sk team,
for helping me with the final stages of this thesis and mostly for inspiring me to work
harder and to seek more and more knowledge.
Last but not least I would like to thank my family for supporting me thru all the
hard times of my studies. I am forever in your debt and I would like to dedicate this thesis
to you.
Abstract
The general aim of this master's thesis is to test the application of satellite radar
imagery on detection of landscape elements and their changes in time. The experiment was
conducted in the vicinity of Bratislava, the capital city of Slovak Republic. Radar imagery
was provided by the TerraSAR-X mission and by the new European Space Agency
initiative, the Sentinel-1 mission. All radar images are inherently degraded by granular
noise known as speckle and before application the very first step is to reduce the amount of
this effect. For this purpose several speckle reduction filters were quantitatively compared
and evaluated on real radar imageries. After speckle reduction, it was possible to perform
object detection. The obtained results were compared with in situ measurements using
global navigation satellite systems. The results have been presented by filtered images,
statistical tables and diagrams.
Key words: Synthetic Aperture Radar, speckle noise reduction, spatial filtering, edge
detection, TerraSAR-X, Sentinel-1
Abstrakt
Hlavným cieľom tejto diplomovej práce je testovanie využitia družicových
radarových snímok na detekciu prvkov krajiny a ich zmien v čase. Experiment bol
zrealizovaný v okolí Bratislavy, hlavného mesta Slovenskej republiky. Radarové dáta boli
poskytnuté z misie TerraSAR-X a novej misie Európskej vesmírnej agentúry, Sentinel-1.
Každá radarová snímka je do určitej miery znehodnotená speckle šumom a pred jej
použitím musí byť tento efekt zredukovaný. K tomuto účelu práca obsahuje kvantitatívne
porovnanie a zhodnotenie viacero speckle filtrov otestovaných na reálnych radarových
snímkach. Po redukcii šumu bolo možné vykonať detekciu prvkov krajiny. Pre
zhodnotenie presnosti detegovaných prvkov boli výsledky porovnané s in situ meraním
pomocou globálnych navigačných satelitných systémov. Výsledky sú prezentované
odšumenými snímkami, štatistickými tabuľkami a grafmi.
Kľúčové slová: Radar so syntetickou apertúrou, redukcia speckle šumu, filtrácia
digitálnych obrazov, detekcia hrán, TerraSAR-X, Sentinel-1
Contents
List of Appendices ............................................................................................................... 11
List of Acronyms and Abbreviations ................................................................................... 12
List of Symbols ................................................................................................................... 14
Introduction .......................................................................................................................... 15
1.
2.
3.
Theoretical Aspects of SAR Technology .................................................................... 16
1.1.
SAR Introduction .................................................................................................. 16
1.2.
Complex SAR Image ............................................................................................ 17
1.3.
Detected SAR Image ............................................................................................. 17
1.4.
Commonly Used Radar Frequencies in RS ........................................................... 18
1.5.
SAR Polarization................................................................................................... 19
1.6.
SAR Viewing Geometry ....................................................................................... 20
Speckle Noise Reduction ............................................................................................. 21
2.1.
Speckle Noise Definition ...................................................................................... 21
2.2.
Speckle Suppression Techniques .......................................................................... 22
2.3.
Tested Filters ......................................................................................................... 22
2.4.
Speckle Filters Evaluation..................................................................................... 23
2.4.1.
Equivalent Number of Looks ......................................................................... 24
2.4.2.
Speckle Suppressions Index........................................................................... 24
2.4.3.
Speckle Suppression and Mean Preservation Index ...................................... 24
2.4.4.
Edge-Enhancing Index ................................................................................... 25
2.4.5.
Feature-Preserving Index ............................................................................... 25
2.5.
Testing SAR Dataset ............................................................................................. 26
2.6.
Speckle Filter Evaluation Results ......................................................................... 27
Object Detection on SAR Images ................................................................................ 31
3.1.
Area of Interest ...................................................................................................... 31
3.2.
TSX Imagery ......................................................................................................... 32
3.2.1.
TSX Mission Description .............................................................................. 32
9
3.2.2.
LAN 1583 Dataset ......................................................................................... 33
3.2.3.
LAN 2833 Dataset ......................................................................................... 34
3.3.
Sentinel-1 Imagery ................................................................................................ 35
3.3.1.
Sentinel-1 Mission Description ..................................................................... 36
3.3.2.
Sentinel 1 Dataset .......................................................................................... 37
3.4.
Edge Detection ...................................................................................................... 38
3.5.
In Situ Measurements ............................................................................................ 43
3.6.
Comparison with In Situ Measurements ............................................................... 44
Conclusion ........................................................................................................................... 52
Resume................................................................................................................................. 53
Bibliography ........................................................................................................................ 56
Appendices........................................................................................................................... 60
10
List of Appendices
Appendix A
Speckle Evaluation Results Part 1
Appendix B
Speckle Evaluation Results Part 2
Appendix C
Speckle Evaluation Results Sorted by ENL
Appendix D
Speckle Evaluation Results Sorted by SSI
Appendix E
Speckle Evaluation Results Sorted by SMPI
Appendix F
Speckle Evaluation Results Sorted by EEI
Appendix G
Speckle Evaluation Results Sorted by FPI
11
List of Acronyms and Abbreviations
AOI
Area of Interest
ASCII
American Standard Code for Information Interchange
C-band
Microwave band, frequency 4-8 GHz, wavelength 3.75-7.5 cm
CEOS
The Committee on Earth Observation Satellites
DLR
German Aerospace Centre
EEI
Edge-Enhancing Index
EM
Electromagnetic
ENL
Equivalent Number of Looks
EPSG
European Petroleum Survey Group
ESA
European Space Agency
EU
European Union
FPI
Feature-Preserving Index
GMES
Global Monitoring for Environment and Security (Copernicus)
GMT
Greenwich Mean Time
GNSS
Global Navigation Satellite Systems
GNU
GNU's not Unix
GPL
General Public License
GUI
Graphic User Interface
HH
SAR polarization, horizontal transmission, horizontal reception
HV
SAR polarization, horizontal transmission, vertical reception
IW
Interferometric Wide Swath Mode
K-band
Microwave band, frequency 18-26.5 GHz, wavelength 11.3-16.7 mm
Ka -band
Microwave band, frequency 26.5-40 GHz, wavelength 5-11.3 mm
Ku-band
Microwave band, frequency 12-18 GHz, wavelength 16.7-25 mm
L-band
Microwave band, frequency 1-2 GHz, wavelength 15-30 cm
MAP
Maximum a Posteriori
NE
Northeast
NMS
Non-maximum Suppression
NW
Northwest
P-band
Microwave band, frequency 0.3-1 GHz, wavelength 30-100 cm
PGM
Portable Graymap Format
PGS
Payload Ground Segment
12
RADAR
Radio Detection and Ranging
RAR
Real Aperture Radar
RS
Remote Sensing
SAR
Synthetic Aperture Radar
S-band
Microwave band, frequency 2-4 GHz, wavelength 7.5-15 cm
SE
Southeast
STBX
Sentinel-1 Toolbox
SM
StripMap imaging mode of the TerraSAR-X satellite
SMPI
Speckle Suppression and Mean Preservation Index
SSI
Speckle Suppression Index
ST
SpotLight imaging mode of the TerraSAR-X satellite
STD
Standard Deviation
STU
Slovak University of Technology in Bratislava
SVF
Faculty of Civil Engineering
SW
Southwest
TIFF
Tagged Image File Format
TSX
TerraSAR-X
VAR
Variance
VH
SAR polarization, vertical transmission, horizontal reception
VV
SAR polarization, vertical transmission, vertical reception
WGS 84
World Geodetic System 1984
X-band
Microwave band, frequency 8-12 GHz, wavelength 25-37.5 mm
XLS
Excel Binary File Format
13
List of Symbols
C
Number of columns in SAR image
f
SAR image of true radiometric values
g
Original image value
g1
Original image pixel values on one side of the edge
g2
Original image pixel values on the other side of the edge
gf1
Filtered image pixel values on one side of the edge
gf2
Filtered image pixel values on the other side of the edge
gf
Filtered amplitude image
n
Kernel dimension
Q
SMPI coefficient
R
Number of rows in SAR image
u
Speckle noise
y
Image column coordinate
x
Image line coordinate
β
Ration of the standard deviation to the mean of a filtered image
σu
Speckle noise standard deviation
14
Introduction
Currently our planet faces an increased threat of natural disasters due to climate
changes caused by either natural processes or human activities. This is the reason why
many countries and intergovernmental organizations such as the European Space Agency
(ESA) are interested in monitoring the state of the environment, oceans and atmosphere.
The goal is to gather accurate and timely information to improve the management of the
environment, understand and mitigate the effects of climate changes and ensure civil
security. The single location where we can learn the most about our planet is found
nowhere on Earth but high up above it (unknown ESA's spokesperson, 2013). A powerful
tool for Earth observation is remote sensing (RS). RS is the science and art of obtaining
information about an object, area, or phenomenon through the analysis of data acquired by
a device that is not in contact with the object, area, or phenomenon under investigation
(Lillesand, Kiefer, Chipman, 2004). Based on the wavelength in which the system
operates, RS is divided into optical and microwave RS.
Microwave RS using Synthetic Aperture Radar (SAR) offers many advantages and
new applications over optical RS but it also has its weaknesses. Microwave radiation can
penetrate through cloud cover and most weather conditions. Because radar is an active
sensor, it can also be used to image the surface at any time, day or night.
All SAR images are inherently degraded by granular noise known as radar speckle.
Speckle is caused by random constructive and destructive interference from multiple
backscatters and causes difficulties for analysis, classification and image interpretation for
various RS applications, including object detection. Before using SAR images, the very
first step is to reduce the effect of speckle. One of the common methods which are used to
reduce speckle noise is spatial filtering. For the purpose of this research, five speckle filters
were quantitatively evaluated. The filter showing the best results was latter used to reduce
the speckle effect on SAR images used for detection of landscape elements.
This thesis is divided into three main chapters. The first chapter contains theoretical
aspects of SAR. The second chapter deals with speckle noise and its reduction techniques.
The goal of this chapter is to choose the most suitable speckle filter, denoise the SAR
images and prepare them for edge detection. Finally, chapter three describes edge detection
and edge extraction from despeckled SAR images. The detected edges were then compared
with more conventional data collection method, the global navigation satellite systems
(GNSS).
15
1. Theoretical Aspects of SAR Technology
SAR is an active microwave imaging system. The microwave portion of the
electromagnetic spectrum covers the range from approximately 1 cm to 1m in wavelength.
These relatively long wavelengths are independent of atmospheric scattering and can
penetrate through clouds, dust, fog or heavy rainfall. Active sensor supplies its own source
of energy to illuminate the target. The advantage gained from the use of active sensors, is
the possibility to acquire data also during the night.
1.1. SAR Introduction
The word radar is an acronym for RAdio Detection And Ranging. As its name
implies, radar was developed as a means of using radio waves to detect the presence of
objects and to determine their distance and sometimes their angular position. The process
entails transmitting short bursts, or pulses, of microwave energy in the direction of interest
and recording the strength and origin of "echoes" or ''reflections'' received from objects
within the system's field of view. Radar systems may or may not produce images, and they
may be ground based, or mounted in aircraft or spacecraft (Lillesand, Kiefer, Chipman,
2004).
SAR transmits pulses of microwave energy towards the Earth's surface and detects
the reflected backscatter from each target. The received backscattered signals are separated
into two components which are carrying information about the amplitude and the phase of
the returning signal. Amplitude depends on target properties (structure and dielectric
properties). Phase is a function of the distance between the sensor and the target as well as
target properties. Together they form a complex number. To create an image, the returning
signal of a single pulse is sampled and these samples are stored in an image line. As the
radar platform moves forward, recording and processing of the backscattered signals build
up a two-dimensional image of the surface.
SAR systems were developed to overcome the limitations of Real Aperture Radar
(RAR) systems. Compared to RAR, SAR synthetically increases the antenna's size to
achieve finer resolution in the platform motion direction. RAR systems have their physical
limit to the length of the antenna that can be carried on an aircraft or satellite. SAR
increases the size of the antenna or aperture electronically.
16
1.2. Complex SAR Image
A SAR image is a two-dimensional array of pixels formed by columns and rows
where a pixel is associated with a small area of Earth's surface whose size depends only on
the SAR system characteristics. Each pixel provides a complex number (amplitude and
phase information) associated to the reflectivity of all the scatters contained in SAR
resolution cell (Lee, Pottier, 2009).
1.3. Detected SAR Image
The detected SAR Image contains a measurement of the amplitude of the radiation
backscattered toward the radar by the objects (scatters) contained in each SAR resolution
cell (Ferreti et al., 2007).
The detected amplitude depends on how the radar energy interacts with the surface,
which is a function of several variables or parameters. These parameters include the
particular characteristics of the radar system (viewing geometry, frequency, polarization,
etc.) as well as characteristics of the surface (land cover type, topography, relief, etc.).
Generally the dominant factor in determining the tones in radar images is surface
roughness. Exposed rocks and urban areas show strong amplitudes, whereas smooth
surfaces like water show low amplitudes, since the radiation is reflected away from the
radar. Amplitude images are generally visualised by means of grey scale levels.
Assume that a detected SAR image g(x,y) is sampled so that the resulting digital
image has R rows and C columns. Figure 1.1 shows the coordinate convention used
throughout this thesis. The values of the coordinates (x,y) now become discrete numbers
quantities. The origin coordinates are (x,y) = (0,0). The next coordinate value along the
first row of the image are represented as (x,y) = (1,0). This knowledge is essential for
proper transformation of the detected landscape elements from image coordinates to
projected map coordinates.
Figure 1.1: Coordinate convention used to represent digital images in this thesis.
17
1.4. Commonly Used Radar Frequencies in RS
Similarly to optical RS, even SAR sensors operate with one or more different
bands. Radar waves fall under the microwave region of the electromagnetic (EM)
spectrum. The microwave region of the EM spectrum is quite large, relatively to the visible
and infrared. There are several wavelength ranges or bands commonly used, which were
given code letters during World War II and remained to this day (CCRS, 2014):
•
Ka, K and Ku bands: very short wavelengths used in early airborne radar systems
but uncommon today,
•
X-band: used extensively on airborne systems for military reconnaissance and
terrain mapping, X-band is also used by the TerraSAR-X satellite, whose data is
utilized in this thesis,
•
C-band: common on many airborne (CCRS Convair-580 and NASA AirSAR) and
spaceborne systems including ERS-1 and 2, RADARSAT , Sentinel-1,
•
S-band: used on board of the Russian ALMAZ satellite,
•
L-band: used on board of the American SEASAT, Japanese JERS-1 satellites,
NASA airborne system, ALOS, ALOS-2, L-band will also be used in the planned
Tandem-L mission,
•
P-band: longest radar wavelengths, used on NASA experimental airborne research
system and also planned for the ESA Biomass mission.
Figure 1.2: Microwave region of the EM spectrum.
(Adapted from (CCRS, 2014))
18
1.5. SAR Polarization
The microwave polarization refers to the orientation of the electric field vector of
the transmitted beam with respect to the horizontal direction. If the electric field vector
oscillates along a direction parallel to the horizontal direction, the beam is said to be
horizontally polarized. On the other hand, if the electric field vector oscillates along a
direction perpendicular to the horizontal direction, the beam is vertically polarized
(CRISP, 2001).
Using different polarizations and wavelengths, it is possible to collect information
that is useful for particular applications, e.g. to classify agricultural fields. In radar system
descriptions the reader can come across the following abbreviations (Tempfli et al., 2009):
•
HH: horizontal transmission and horizontal reception,
•
VV: vertical transmission and vertical reception,
•
HV: horizontal transmission and vertical reception,
•
VH: vertical transmission and horizontal reception.
Older SAR sensors were only capable to provide single polarization channel
images. Modern SAR sensors are often dual-polarized or even capable of providing full
polarization datasets (HH, HV, VV, VH).
Figure 1.3: Example of few SAR satellites and their polarization capabilities.
(Adapted from (MDA, 2014))
19
1.6. SAR Viewing Geometry
SAR imaging geometry is different from systems employed for optical RS. SAR
sensors are side-looking instruments while most optical instruments are nadir-looking.
This difference exists because optical sensors are able to distinguish among targets using
angular distance from the sensor. SAR can only distinguish the returns from various targets
based upon the arrival time of the received signals. A nadir-looking radar could not
distinguish between two scatters with equal distances from the sensor because a single
incident wave front illuminates both points at the same instant, so the scatter returns from
both points arrive at the same receiving antenna simultaneously (Austin Center for Space
Research, 2015).
SAR viewing geometry is explained in Figure 1.4. The SAR platform (A) travels
forward along the flight path or orbit (B) with the nadir directly beneath the platform (C).
The projection of the flight path on the surface is called the ground track (D). The
microwave beam illuminates an area or swath (E). The along-track dimension parallel to
the flight direction refers to azimuth (F), while the cross-track dimension refers to range
(G). The portion of the image swath closest to the ground track of the radar platform is
called the near range while the portion of the swath farthest from the ground track is called
far range. The look angle (H) is the angle at which the radar looks at the surface. The
incidence angle (I) of the system is defined as the angle between the radar beam and the
local surface vertical. Moving from near range to far range, the incidence angle increases.
At all ranges the radar antenna measures the radial line of sight distance between the radar
and each target on the surface. This line is called slant range (J). The true horizontal
distance along the ground corresponding to each measured point in slant range is called
ground range (K).
Figure 1.4: SAR viewing geometry.
20
2. Speckle Noise Reduction
Unlike optical RS images, characterized by very neat and uniform features, SAR
images are affected by speckle noise. Speckle forms a main obstacle to analyse, interpret
and classify SAR images for various RS applications. Figure 2.1 displays a subset of a
detected SAR image degraded by speckle noise.
Figure 2.1: Detected SAR image: the speckle effect on the homogenous
fields is clearly visible.
2.1. Speckle Noise Definition
Speckle is a direct result of the fact that the incident energy is coherent - that is, it
can be assumed to have a single frequency and the wavefront arrives at a pixel with a
single phase. If there were a single large dominant scatter in the pixel, such as a corner
reflector or a building, then the returned signal would be largely determined by the
response of that dominant element, and any scattering from the background would be
negligible. More often, though the pixel will be a sample of very large number of
incremental scatters; their returns combine to give the resultant received signal for that
pixel (Richards, 2009). Such situation is illustrated in Figure 2.2.
Figure 2.2: Generation of speckle through the interference of a very large number of rays
scattered from within a pixel. (Adapted from (Richards,2009))
21
Speckle can be statistically characterized by multiplicative noise model. Degraded
image with speckle noise is given by the equation (Sarode, Deshmukh, 2011):
g ( x , y ) = f ( x, y ) ⋅ u ( x, y )
(2.1)
Where, g (x,y) is the observed amplitude image with speckle, f (x,y) is the image of
the true radiometric values and u (x,y) is the speckle noise. (x,y) denotes the pixel location.
The noise is characterized by a distribution with a unit mean (E[u]=1) and a standard
deviation σu. The multiplicative nature of speckle complicates the noise reduction process.
2.2. Speckle Suppression Techniques
Generally speaking, speckle noise can be reduced by multi-look processing or
spatial filtering (Raney, 1998). Multi-looking or multiple-look processing is usually done
during data acquisition and speckle reduction by spatial filtering is performed on the output
image in a digital image analysis environment. Spatial filtering is a neighbourhood
operation which works with the values of the image pixels in the neighbourhood and the
corresponding values of a subimage that has the same dimensions as the neighbourhood.
The subimage is called a filter, mask, kernel, template, or window, with the first three
terms being the most prevalent terminology. The values in a filter subimage are referred to
as coefficients, rather than pixels (Gonzales, 2008). The mask of a few pixels in dimension
(e.g. 3x3, 5x5, etc.) is moving over each pixel in the image, applying a mathematical
calculation using the pixel values under that mask, and replacing the central pixel with the
new value. The kernel is moved along the image one pixel at a time until the entire image
has been covered. By applying the filter a smoothing effect can be achieved and the visual
appearance of the speckle is reduced. The ideal filter should preserve object edges, textural
and radiometric information. This thesis evaluates the effect of Frost, Lee, Refined Lee,
Gamma MAP and Perona-Malik equation based filter.
2.3. Tested Filters
This subchapter contains basic information about the evaluated speckle filters. For
more detailed description and mathematical principles, readers are referred to relevant
publications quoted in the text.
The Frost filter replaces the pixel of interest with a weighted sum of the values
within the n x n moving kernel. It is based on the local statistics and the multiplicative
noise model. The weighting factors decrease with distance from the pixel of interest. They
22
increase for the central pixels as variance within the kernel increases with the edge areas to
preserve edge structure (Mansourpour, Rajabi, Blais, 2006).
Gamma or maximum a posteriori (MAP) filter minimizes the loss of texture
information in gamma-distributed scenes which are the images of forested areas,
agricultural land and oceans. In this filtering technique, the smoothing process is
determined by the coefficient of variation and contrast ratios of probability density
functions (Shanthi, Valarmathi, 2013).
The Lee filter utilizes the statistical distribution of digital number within the
moving kernel to estimate the value of pixel of interest. This filter assumes Gaussian
distribution for the noise in image data (Lee, 1981). The Lee filter removes the noise by
minimizing either the mean square error or the weighted least square estimation.
Refined Lee Filter is based on the Lee filter. It considers a Local Linear Minimum
Mean Square Error estimation within edge aligned windows, achieving a better
preservation of the image details.
Perona-Malik filter was tested with the cooperation of Department of Mathematics,
Faculty of Civil Engineering (SVF), Slovak University of Technology in Bratislava (STU).
Perona-Malik nonlinear image selective smoothing equation (called anisotropic diffusion
in image processing) was proposed in 1990 by Perona and Malik. It usually works well for
images disturbed by additive noise, provided that the gradients of noise are smaller than
the edge gradients. The multiplicative character of the speckle is changed into additive one
by logarithming. The explored algorithm works on an adaptive grid, i.e., for homogenous
areas it uses larger elements of the computational grid. This filter does not preserve
radiometric properties, its aim is to support edge detection.
2.4. Speckle Filters Evaluation
To assess the capability of the tested filters to remove speckle noise, and their
effectiveness in successfully preserving the real structure of the scene backscatter, several
performance measures were used. The following measures were also used to assess the
performance of several iterations of filtering and also different kernel sizes 3x3, 5x5, and
7x7. This doesn't apply to Perona-Malik filter, which works on an adaptive grid. Also
Refined Lee filter was unable to set a different kernel size than 7x7 pixels. In addition to
these measures visual inspection also provides a good assessment of the speckle filters
23
performance. It was an efficient and easy way to assess the speckle reduction capability
and also effectiveness in edge preserving.
2.4.1. Equivalent Number of Looks
Equivalent Number of Looks (ENL) for amplitude image is calculated using the
following equation (Lee et al. 1994), (Lee, Pottier, 2009):
ENL = ( 0.5527 / β )
2
(2.2)
Where 0.5527 is the value of the σu of a 1-look amplitude SAR image. This definition is
consistent with the multi-look processing using amplitude averaging of individual looks.
According to (Lee et al., 1994) and (Lee, Pottier, 2009) β is the ration of the standard
deviation to the mean of the image and it is defined as the coefficient of variance (speckle
index):
var( g f )
β=
E  g f 
(2.3)
Where gf is the pixel value of the filtered SAR image.
2.4.2. Speckle Suppressions Index
The Speckle Suppression Index (SSI) is the coefficient of variance of the filtered
image normalized by that of the original image, which is defined as (Sheng, Xia, 1996):
SSI =
var( g f )
E  g f 
⋅
E[g]
var( g )
(2.4)
Where gf is the filtered value, g is the original value. For most cases SSI < 1.0, which
means speckle is suppressed. The lower the SSI is, the stronger suppression ability the
filter has.
2.4.3. Speckle Suppression and Mean Preservation Index
According to (Shamsoddini, Trinder, 2010) ENL and SSI are not reliable when the
filter overestimates the mean value. They have developed an index called Speckle
Suppression and Mean Preservation Index (SMPI). The equation of this index is as follow:
SMPI = Q ⋅
24
var( g f )
var( g )
(2.5)
And Q is calculated as:
Q = 1 + E [ g ] − E [ gˆ 0 ]
(2.6)
The lower values indicate better performance of the filter in terms of mean preservation
and noise reduction.
2.4.4. Edge-Enhancing Index
The Edge-Enhancing Index (EEI) is an important parameter in assessing the
enhancement of step functions in an image. It is often used to evaluate a filters's ability to
preserve edges, such as boundaries between water bodies and land. The EEI is defined as
(Qiu et al., 2004):
EEI =
∑g
∑g
f1
1
− gf2
− g2
(2.7)
Where g1 and g2 are the original values of the pixels on either side of the edge, whereas gf1
and gf2 are the corresponding filtered values. The numerator is the absolute difference in
intensity of the pixels on the two sides of the edge in the filtered image and the
denominator is that difference in the original image. Therefore, the EEI is usually smaller
than 1.0 and higher EEI values correspond to a better edge preserving capability.
2.4.5. Feature-Preserving Index
Feature-Preserving Index is a measure for assessing a filter's ability to preserve
linear features and subtle structures. For a one-pixel wide linear feature of n-pixel length,
the FPI is given by (Qiu et al., 2004):
∑ (2⋅ g
n
FPI =
1
f
− gf1 − gf 2 )
n
∑ (2 ⋅ g − g
1
− g2 )
(2.8)
1
Where g is the original value of a pixel on the linear feature. g1 and g2 are the
original values of the neighbouring pixels on both sides of the feature, and gf , gf1 and gf2
are the filtered values of corresponding pixels. In most cases, the values of FPI measure are
also lower than 1. The higher the FPI measure, the better the linear feature.
25
2.5. Testing SAR Dataset
The test image is a 4000 x 4000 pixel subset from a detected SAR image. It was
selected from the dataset intended for project ID LAN1583: Object Recognition
Recogni
Based on
High-Resolution
Resolution Radar Imagery. The dataset was kindly provided by the German
Aerospace Center (DLR). The image was acquired by the TerraSAR-X
TerraSAR (TSX) mission
(X-band,
band, 31 mm wavelength) on December 3, 2008 with Spotlight acquisition mode, VV
polarization
ation and sample spacing 0.75 m. In order to test the particular filters with ENL, SSI
SS
and SMPI, a homogenous segment was selected from the SAR image. The area is
represented with the purple rectangle in Figure 2.3.
2.3 For edge preservation testing with EEI
and FPI indices, a cross section of the agricultural field's edge was selected. It is
represented in Figure 2.3 with the small yellow line.
Figure 2.3: Subset of the TerraSAR-X
TerraSAR image from December 3, 2008.
2008 The purple rectangle
shows the selected homogeneous area, the yellow line represents the edge cross section.
26
2.6. Speckle Filter Evaluation Results
Initial processing and speckle reduction was performed in Sentinel-1 Toolbox
(S1TBX). S1TBX was developed under contract to ESA by Array Systems Computing Inc.
and contributors. The toolbox and full source code is distributed freely under the GNU
GPL license. The S1TBX is consisting of a collection of processing tools, data product
readers, writers and a display and analysis applications to support data from ESA SAR
missions including Sentinel-1 and third party SAR data from TerraSAR-X (S1TBX Help,
2015). S1TBX is programmed in Java, it provides user friendly graphic user interface
(GUI) and supports Windows, Linux and MAC OS X operating systems. S1TBX was used
mostly for reading Sentinel-1 and TSX data, creating subsets, speckle filtering, map
projection and GeoTiff export.
The Perona-Malik filter was a standalone program written in C programming
language. The program enables to read ASCII images in Portable Graymap Format (PGM)
and set different parameters of the smoothing effect.
Manually calculating all parameters for each smoothed image would be time
consuming with a greater possibility of calculation errors. A simple MATLAB function
was created to overcome this problem. The function is called Speckleeval. This function
quickly and easily automates the calculation. Speckleeval reads both the original image and
the smoothed image, calculates all evaluation parameters and writes the results into Excel
Binary File Format (XLS).
Mean values, variations (VAR), standard deviations (STD) and all performance
measures mentioned above were computed for 55 smoothed images. Generally increasing
the filters kernel size and the number of iterations improved speckle reduction but
decreased edge preservation. Numerical results for each filter and variable kernel sizes are
presented in Appendix A and Appendix B. For example label 3Frost1 means that the SAR
image was smoothed by one iteration of Frost filter using a 3x3 pixel size kernel. Same
labelling was used in the following Appendices.
After speckle reduction all smoothed SAR images were submitted to edge detection
with the Canny Edge Detection Algorithm. The most suitable results from edge detection
were achieved on images smoothed by applying a 7x7 pixel size kernel, so only these
results were compared including Perona-Malik filter with adaptive grid. All results sorted
by each measure are listed in Appendices C to G. Best speckle reduction effect was
achieved by Gamma MAP filter according to ENL, SSI and SMPI indices. Lee and Frost
27
filter's ability to reduce speckle after 5 iterations was nearly identical to Gamma MAP
filter. Poorest results were achieved by Refined Lee filter and Perona-Malik filter after 5
iterations. Overall, all filters except Perona-Malik are capable of preserving the mean of a
homogenous segment well. Emphasis of this thesis is on edge detection, therefore EEI and
FPI indices have greater weight. Appendix F shows numerical values of the mean, VAR,
STD and all performance measures of all 5 adaptive speckle reduction filters sorted by EEI
in descending order. Appendix G shows results sorted by FPI also in descending order. The
higher EEI and FPI indices, the better edge preservation. From Appendix F and Appendix
G it is clear that best results in preserving edges were obtained by Refined Lee and PeronaMalik filter. Although according to EEI the Perona-Malik filter after 5 iterations performed
worse than all other filters, on average it still performs exceptionally well.
To demonstrate the smoothing effect of the speckle filters, close-up of the original
image and the same image smoothed by 5 iterations of Lee, Frost, Gamma MAP, Refined
Lee (with kernel size 7x7 pixels) and by 40 iterations of Perona-Malik filter are displayed
in figure 2.4. First of all, it can be clearly seen that speckle noise is significantly reduced in
the filtered images. Secondly, although the filtered images appear to be blurry within a
land cover segment, differences between neighbouring land covers are visibly enhanced.
28
(b)
(a)
(c)
(d)
(e)
(f)
Figure 2.4: Original image (a) and smoothed images by Lee (b), Gamma MAP (c),
Refined Lee (d), Perona-Malik after 40 iterations (e) and Frost filter (f).
This thesis focuses on landscape objects detection, so the main criterium is to
choose the filter with the best edge preserving capability. It would seem that there are two
obvious candidates Refined Lee and Perona-Malik filter. However applying the edge
detector and comparing the detected edges revealed some interesting new facts. The
situation is shown in Figure 2.5. For example, the detected edge on Gamma MAP filtered
image was much smoother than the edge detected on the Perona-Malik smoothed image.
29
(b)
(a)
(c)
(d)
Figure 2.5: Smoothed images by Gamma MAP filter (a), Perona-Malik filter (b), and
detected edges on Gamma MAP smoothed image (c) and Perona-Malik smoothed image
(d).
Despite the numerical evaluation of all filters, visual comparison proofed to be the
most reliable measure. For this reason, the final decision was made in favour of the
Gamma MAP filter. Except the tests presented in this thesis, numerous other test were
conducted on different SAR images bringing different results. The conclusion is that there
is no single best filter for every possible scenario. The most suitable filter depends on
many parameters and variables e. g. SAR viewing geometry, polarization, land cover,
prerequisites for certain applications etc.. Also the most reliable and quickest measure for
selecting the most suitable filter for images being processed in practical use is visual
assessment.
30
3. Object Detection on SAR Images
This experiment is focused on edge detection and edge extraction of landscape
elements in SAR images. The detection was performed on multiple SAR images including
TSX and Sentinel-1 radar satellite missions within the vicinity of Bratislava, the capital
city of Slovakia.
3.1. Area of Interest
The area of interest (AOI) is situated northeast of the capital city Bratislava next to
class II road 503 between the D1 highway and the city of Pezinok. This location was
selected due to multiple available SAR images of this area and for its good accessibility via
the D1 highway. The study area is located in the Danubian Lowland at the intersection of
Danubian Hills and Danubian Flat. The AOI's center geographic coordinates are 48° 14´
52´´ N and 17° 19´ 20´´ E in World geodetic system 1984 (WGS 84). According to the
territorial and administrative system of Slovakia it is situated in Bratislava Region, Pezinok
District and it falls under the cadastral areas of Slovenský Grob and Viničné.
The AOI (Figure 3.1) consists of an agricultural field with the size of 219 hectares.
The field is bounded on the northeast (NE) by an asphalt road, on the northwest (NW) by
an alley of trees, on the southwest (SW) by a field road and on the southeast (SE) side it is
bounded by various border types such as trees, field roads and more agricultural fields.
These different borders are essential for evaluating the SAR's and edge detector's ability to
recognize edges of landscape elements.
Figure 3.1: Area of interest. (Adapted from Google Earth)
31
3.2. TSX Imagery
The TerraSAR-X images analyzed in this experiment were obtained through
German Aerospace Center (DLR) project ID LAN1583: Object Recognition Based on
High-Resolution Radar Imagery and project ID LAN2833: Utilization of the High SpatioTemporal Resolution of TerraSAR-X Observations for Recent Ground Deformation
Monitoring and SAR Image Processing. More information about these projects is available
at the TerraSAR-X Science Service System. For the purposes of the experiment one image
from project LAN1583 and two images from LAN2833 were selected.
3.2.1. TSX Mission Description
TerraSAR-X is a joint project between the DLR and the German industry
(ASTRIUM). Operational data acquisition for the TerraSAR-X mission is executed by two
satellites TSX-1 (in orbit since June 2007) and TDX-1 (launch in June 2010). Both
instruments will also fulfill the TanDEM-X mission in a joint operation. DLR owns and
operates the satellites and the payload ground segment (PGS) and holds the rights for the
scientific exploitation of the TerraSAR-X mission data (Fritz, Eineder, 2013).
The TSX-1 is a side-looking X-band SAR based on active phased array antenna
technology. Figure 3.2 illustrates an artist's view of the satellite. Characteristic values of
the platform and SAR instrument are summarized in Table 3.1. In June 2010, TSX-1 was
supplemented in orbit by its twin, the TanDEM-X instrument.
Figure: 3.2: Artist's view of TerraSAR-X.
Adapted from (DLR, 2015)
32
Table 3.1: TerraSAR-X technical facts.
TerrSAR-X technical facts
5 years, for both satellites (TerraSAR-X and
TanDEM-X), an extended lifetime of that least
Operational life
another 5 years (beyond 2018) is expected by
the operator DLR (Status: April 2014).
Orbit
Sun-synchronous repeat orbit
Repeat period
11 days
Inclination
97.44°
Altitude at the equator
514 km (319.8 miles)
Centre Frequency
9.65 GHz (X band)
Nominal acquisition direction
Right side
Single, dual - depending on imaging mode
Polarization
quadruple is available as advanced polarization
mode for dedicated acquisition campaigns
(Adapted from TerraSAR-X Image Product Guide, 2014)
3.2.2. LAN 1583 Dataset
The TSX data was delivered as basic image products. They correspond to the
CEOS Level 1b quality. TSX imagery from project LAN 1583 was acquired with the
SpotLight (ST) imaging mode. Spotlight imaging modes uses phased array beam steering
in azimuth direction to increase the illumination time, i.e. the size of the synthetic aperture.
(TerraSAR-X Image Product Guide, 2014). Figure 3.3 shows the image subset of the AOI.
and Table 3.2 summarizes the basic metadata of the image.
Figure 3.3: Preview of the TSX image of the AOI from December 3, 2008.
33
Table 3.2: Basic metadata of the TSX image acquired on December 3, 2008.
Image metadata
Product type
Acquisition mode
Beams
Acquisition date
Cycle
Track
Orbit
Pass
Sample type
Polarization
Range sample spacing
Azimuth sample spacing
Pulse repetition frequency
Radar frequency
Total size
Raster height
Raster width
Average scene height ellipsoid
Map projection applied
SE_SL_S
Spotlight
Spot_066
03-Dec-2008
49
146
8162
Ascending
Detected
VV
0.75 m
0.75 m
3757 Mhz
9650 MHz
922 Mb
23333 lines
20666 samples
268.394 m
UTM zone 33N
3.2.3. LAN 2833 Dataset
LAN 2833 was acquired with the StripMap (SM) mode. Stripmap is the basic SAR
imaging mode known, e.g. ERS-1 and other radar satellites. The ground swath is
illuminated with continuous sequence of pulses while the antenna beam is fixed in
elevation and azimuth. This results in an image strip with a continuous image quality (in
flight direction) (TerraSAR-X Image Product Guide, 2014). Previews of the two images
used in the practical experiment are shown in Figure 3.4 and Figure 3.5. Table 3.3
summarizes the basic metadata of the images. The fist image from this dataset was
acquired five years after the initial image from LAN1583 and the second image after
another three months, both with HH polarization.
Figure 3.4: Preview of the TSX image of the AOI from January 21, 2013.
34
Figure 3.5: Preview of the TSX image of the AOI from June 2, 2013.
Table 3.3: Basic metadata of the TSX images from the LAN2833 dataset.
Image metadata
Product type
EEC_SE_SM_S
Acquisition mode
StripMap
Beams
Strip_007
Acquisition date
21-JAN-2013
Cycle
187
Track
17
Orbit
31079
Pass
Descending
Sample type
Detected
Polarization
HH
Range sample spacing
1.25 m
Azimuth sample spacing
1.25 m
Pulse repetition frequency
3437 Mhz
Radar frequency
9650 MHz
Total size
3439 Mb
Raster height
52400 lines
Raster width
34800 samples
Average Scene height ellipsoid
228.715 m
Map projection applied
UTM zone 33N
EEC_SE_SM_S
StripMap
Strip_007
02-JUN-2013
199
17
33083
Descending
Detected
HH
1.25 m
1.25 m
3437 Mhz
9650 MHz
3479 Mb
52400 lines
34800 samples
228.728 m
UTM zone 33N
3.3. Sentinel-1 Imagery
Sentinel 1 data was acquired from the Sentinel-1 Scientific Data Hub. The Sentinel
1 Scientific data Hub provides free and open access to a Rolling Archive of Sentinel-1
Level-0 and Level-1 use products. Access to the data hub is granted after free one time
registration.
35
3.3.1. Sentinel-1 Mission Description
Sentinel-1 is a polar-orbiting satellite for operational SAR applications. The
constellation of two C-band radar satellites will provide continuous all weather day/night
imagery for user services, especially those identified in ESA's GMES (Copernicus) service
elements programme and on projects funded by the European Union (EU) (ESA Bulletin,
2007). Sentinel-1 satellites are being built by an industrial consortium led by Thales Alenia
Space (Italy) as the prime Contractor, while ASTRIUM (Germany) is responsible for the
C-band SAR payload. Figure 3.6 illustrates an artist's view of the Sentinel-1 satellite.
Technical parameters of the satellite are summarized in Table 3.4.
Figure: 3.6: Artist's view of Sentinel-1.
Adapted from (www.esa.int, 2015)
Table 3.4: Sentinel-1 technical facts.
Sentinel-1 technical facts
Operational life
Orbit
7 years with consumables for 12 years
Near-polar, Sun-synchronous, circular
12 days repeat cycle (1 satellite), 6 days
Repeat period
(constellation)
Inclination
98.18°
Altitude at the equator
698 km (433.7 miles)
Centre Frequency
5.405 GHz (C band)
Nominal acquisition direction
Right side
Polarization
Single and dual polarization for all modes
(Adapted from Sentinel-1 User Handbook, 2014)
36
3.3.2. Sentinel 1 Dataset
The downloaded product specification is Level-1 Ground Range Detected (GRD)
acquired by the Interferometric Wide Swath Mode (IW). The product consists of focused
SAR data that has been detected, multi-looked and projected to ground range. Ground
range coordinates are the slant range coordinates projected onto the ellipsoid of the Earth.
Pixel values represent detected amplitude. Phase information is lost. The resulting product
has approximately square resolution pixels and square pixel spacing with reduced speckle
at a cost of reduced geometric resolution. (Sentinel Online, 2015). Tested data vas
available in both VV (Figure 3.7) and VH (Figure 3.8) polarization with 10 m spacing. The
data was acquired on March 7, 2015.
Figure 3.7: Sentinel-1 image of the AOI from March 7, 2015, VV polarization.
Figure 3.8: Sentinel-1 image of the AOI from March 7, 2015, VH polarization.
37
Table 3.5: Basic Metadata of the Sentinel-1 images acquired on March 7, 2015.
Image metadata
Product type
Acquisition mode
Beams
Processed time
Cycle
Track
Orbit
Pass
Sample type
Polarization
Range sample spacing
Azimuth sample spacing
Pulse repetition frequency
Radar frequency
Total size
Raster height
Raster width
Average scene height ellipsoid
Map projection applied
GRD
IW
07-MAR-2015
42
124
4921
Descending
Detected
VH, VV
10 m
9.996 m
5.40500045433435E9 MHz
3285 Mb
16697 lines
25764 samples
268.394 m
-
3.4. Edge Detection
Edge detection refers to operation or multiple operations performed on an image to
detect edges. Edges are significant local changes of intensity in an image. Usually they
characterize boundaries between two different regions and are therefore a problem of
fundamental importance in image processing.
Since edge detection has been studied, several techniques for edge enhancement
were proposed. Robust edge detection techniques are essentially based on the following
two steps: edge enhancement and decision. Unlike optical images, in SAR data, which is
highly heterogeneous, a robust edge enhancement phase is critical in providing acceptable
detection rates. This phase is usually performed through techniques that are related to
derivation, namely simple differences, Sobel filter (Pratt, 1978), Roberts filter, Prewitt
filter (Prewitt, 1970), morphological gradients etc., possibly combined with smoothing.
However there are three problems by the use of derivative as an edge detector (Prasad,
Rao, Chandrasekhar, 2013):
•
False edges produced by noise are enhanced,
•
ramp edges produce lower derivative magnitude,
•
the location of the ramp edges are not accurate.
38
Canny edge detection algorithm (Canny, 1986) provides tools to overcome these
edge detection problems by applying the derivative to edge detection.
Edge detection and edge extraction was achieved by MATLAB Image Processing
Toolbox. For the purpose of edge detection and extraction from georeferenced SAR
images in GeoTiff format a simple MATLAB function was proposed. The function is
called Saredge. This function enables reading images in GeoTiff format (both optical and
SAR), perform edge detection on greyscale images and export map coordinates of every
single pixel belonging to the detected edge in text file format.
The function stores both image data and a spatial referencing object. Edge
detection is executed by the built-in MATLAB Canny Edge Detection Algorithm. The
Canny edge detector first applies Gaussian smoothing on the image to eliminate remaining
noise. Secondly it computes the squared gradient magnitude. Then it applies nonmaximum suppression (NMS) to get rid of spurious response to edge detection. After NMS
it applies double thresholding to determine potential edges. Local maxima above the first
threshold are identified as weak edges and local maxima above the second threshold are
identified as strong edges. It finalizes the detection by hysteresis. Edges that are weak and
not connected to strong edges are suppressed.
Edge detection returns a binary image containing ''1'' where the function finds edges
in the input image and ''0'' elsewhere. Image coordinates of every pixel containing ''1'' are
then transformed to map coordinates using the stored reference object.
Saredge was gradually applied to all test SAR images previously smoothed by
Gamma MAP filter. The criterion was to detect maximum number of true edges while
detecting minimum number of false edges. Every image required a different threshold
setting to achieve the best possible results. The best results for each SAR image are shown
in figures 3.9 - 3.13.
39
Figure 3.9: Detected edges on the TSX image from December 3, 2008.
Figure 3.10: Detected edges on the TSX image from January 21, 2013.
40
Figure 3.11: Detected edges on the TSX image from June 2, 2013.
Figure 3.12: Detected edges on the Sentinel-1 image with VV polarization from
March 7, 2015.
41
Figure 3.13: Detected edges on the Sentinel-1 image with VH polarization from
March 7, 2015.
Edge detection on the TSX SAR image from December 2008 brought the best
results. All desired edges were identified correctly with a small number of false edges
identified in the homogenous areas due to remaining speckle noise.
Detection on the TSX image from January 2013 did not bring such effective
results. During the sensing period in January 2013 the AOI was apparently covered with
snow. Snow cover is a smooth surface and therefore most of the radiation was reflected
away from the SAR sensor. Because of this the image appears with dark tones and dull
edges, which were not recognisable by Saredge. On the other hand edges detected on the
TSX image from June 2013 are comparable with results obtained from December 2008. By
comparing figures 3.9 and 3.11 it is clearly visible that the original single agricultural field
is now divided into two new agricultural fields.
The attempt to detect edges in Sentinel-1 imagery met with the poorest results and
the edges were incomparable with the TSX dataset. The main drawback of the Sentinel-1
imagery was the lower resolution then the TSX imagery resolution. Also it is clearly
visible that the VH polarised image contains lower amplitudes. The best and only usable
results were achieved on the TSX SAR images from December 2008 and June 2013.
42
3.5. In Situ Measurements
For evaluation of edge detection accuracy it was necessary to perform in situ
measurements in the AOI. The borders of the AOI were mapped with Real Time
Kinematic (RTK) GNSS method on March 20, 2015 with three TOPCON GNSS receivers.
Two receivers TOPCON Hiper GD were used in base/rover setup and the receiver
TOPCON Hiper II was connected to the Slovakian Spatial Observation Service (SKPOS)
which represents active geodetic controls in Slovakia. The border was mapped with
approximately 50 meter step. Single types of borders are shown in Figure 3.14 and Figure
3.15 from (a) to (d). The point coordinates were observed in the geographic coordinate
system WGS 84 (EPSG: 4326). The provided SAR products were projected in the
coordinate system UTM zone 33N (EPSG: 32633). Projection of the observed points was
carried out in Arcmap. The observed points are displayed on the background of the TSX
image in Figure 3.15.
(a)
(b)
(c)
(d)
Figure 3.14: Representative images of the NW border (a), SW border (b),
SE border (c), NE border (d).
43
Figure 3.15:: Points observed with Real-Time
Real Time Kinematic method displayed on the background
of the TSX SAR image.
3.6. Comparison with In Situ Measurements
The points observed by GNSS served as a flawless reference for the detected edges.
The detected edges consist of many single points. These points represent pixels that were
identified as edges. The nearest detected points to the in situ observed points were
we selected
and the differences between them were computed. Note that the SAR images
image were acquired in
2008 and 2013 while the in situ measurements took place in 2015. Since 2008
20 the boundaries
of the AOI may have slightly changed due to agricultural activity. The differences between
GNSS observations and edges detected on the TSX imageries are displayed in
i figures 3.16 3.23. Overlay of the differences from both images is displayed in figures 3.24
3.2 - 3.27.
During the results analysis of the northeast border,, a slight systematic shift was
recorded in both SAR images. Five
Fiv meter shift in image from 2008 and 6 meter shift in image
from 2013. The shift may have been caused by the SAR imaging geometry and by the
microwave's physical nature. The main road is surrounded by trees and ditches on both sides.
side
One possible explanation is that the detected amplitude may have been detected from the
treetops. These systematic shifts were eliminated and the results are shown in figures 3.16
3.1 and
3.17.
44
A slight shift can be also observed on the northwest border shown in figures 3.18 and
3.19. This border consists from alley of trees and this shift may have been caused by the
subjective determination of the border during in situ measurements.
The southeast and southwest borders consisted of different land cover types varying
from field roads, trees and agricultural fields. The larger differences on the southeast and
southwest may have been caused by information loss durinng speckle reduction. We can not
also exclude that the agricultural fileds were differently plowed, however it is highly unlikely
that the differences were so significant.
The results imply that edge detection on SAR images can't compete with automatic
detection on optical remote sensing images due to the distortive effect of the speckle noise.
However it can still find its use in aplications that don't require fine accuracy such as
agriculture or forestry. Such an example can be provided by figures 3.9 and 3.11. The first
image from December 2008 shows an uncultivated single large field. The second image from
June 2013 shows that the field was divided into two sepperated fields. By utilizing multi
polarised datasets in radar polarimetry it is also possible to identify the crops.
Another possible application that doesn't require fine accuracy is monitoring river
banks, floads, icerberg shifts or even pre-earthquake and post-earthquake evaulation. Also
very commonly are detected SAR images utilized in oil spill detection or ship detection.
NE Border, December 2008
1.5
Difference (m)
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Point ID
Figure 3.16: Differences between observed points and detected edges of the NE border from
December 2008, Mean = 0.00 m, STD = 0.75 m.
45
NE Border, June 2013
5
4
Difference (m)
3
2
1
0
-1
-2
-3
-4
-5
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Point ID
Figure 3.17: Differences between observed points and detected edges of the NE border from
June 2013, Mean = 0.00 m, STD = 2.46 m.
NW Border, December 2008
10
Difference (m)
8
6
4
2
0
-2
-4
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Point ID
Figure 3.18: Differences between observed points and detected edges of the NW border from
December 2008, Mean = 3.62 m, STD = 1.98 m.
46
NW Border, June 2013
20
Difference (m)
15
10
5
0
-5
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Point ID
Figure 3.19: Differences between observed points and detected edges of the NW border from
June 2013, Mean = 5.65 m, STD = 4.36 m.
SE Border, December 2008
8
Difference (m)
6
4
2
0
-2
-4
-6
-8
-10
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Point ID
Figure 3.20: Differences between observed points and detected edges of the SE border from
December 2008, Mean = -1.61 m, STD = 3.48 m.
47
SE Border, June 2013
20
15
Difference (m)
10
5
0
-5
-10
-15
-20
-25
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Point ID
Figure 3.21: Differences between observed points and detected edges of the SE border from
June 2013, Mean = -4.30 m, STD = 9.39 m.
SW Border, December 2008
6
5
Difference (m)
4
3
2
1
0
-1
-2
-3
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Point ID
Figure 3.22: Differences between observed points and detected edges of the SW border from
December 2008, Mean = 1.45 m, STD = 1.77 m.
48
SW Border, June 2013
15
Difference (m)
10
5
0
-5
-10
-15
-20
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Point ID
Figure 3.23: Differences between observed points and detected edges of the SW border from
June 2013, Mean = -1.60 m, STD = 4.94 m.
NE Border
TSX December 3, 2008
TSX June 2, 2013
5
4
3
Difference (m)
2
1
0
-1
-2
-3
-4
-5
1
3
5
7
9
11
13
15
17
19
21
23
25
Point ID
Figure 3.24: Overlay of the differences between observed points
and detected edges of the NE border.
49
27
29
NW Border
TSX December 3, 2008
TSX June 2, 2013
20
Difference (m)
15
10
5
0
-5
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
Point ID
Figure 3.25: Overlay of the differences between observed points
and detected edges of the NW border.
SE Border
TSX December 3, 2008
TSX June 2, 2013
20
15
Difference (m)
10
5
0
-5
-10
-15
-20
-25
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47
Point ID
Figure 3.26: Overlay of the differences between observed points
and detected edges of the SE border.
50
SW Border
TSX December 3, 2008
TSX June 2, 2013
15
10
Difference (m)
5
0
-5
-10
-15
-20
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
Point ID
Figure 3.27: Overlay of the differences between observed points
and detected edges of the SW border.
51
31
33
Conclusion
The goal of this thesis was to test object detection on SAR images. Due to the physical
nature of microwave radiation and SAR imaging properties all radar images are inherently
degraded by speckle noise. Five adaptive speckle filters were explored to find the most
acceptable one for denoising speckle and preparing SAR images for edge detection.
The main criterium was to reduce speckle noise and to preserve sharpness of the edges
as much as possible. Quantitative assessment showed interesting results. According to ENL,
SSI and SMPI measures the best speckle reduction was achieved by Gamma MAP filter, note
that the differences between Gamma MAP, Frost and Lee filter where negligible. The filters
with the best edge preservation ability according to EEI and FPI were Refined Lee and
Perona-Malik. Practical edge detection questioned these results. Best edge detection results
were achieved on images that have been previously smoothed by Gamma MAP filter however
results on different SAR datasets may vary. The best filter depends on many parameters and
variables of the SAR system and the observed surface.
A simple MATLAB function for extracting edges from images in GeoTiff format was
proposed. The function was tested on TSX and Sentinel-1 datasets. The results have shown
that the main prerequisite for edge detection is an effective speckle reduction filter and high
resolution of the SAR images. While the TSX datasets were available in 0.75 m and 1.25 m
sample spacing, the freely available Sentinel-1 dataset had only 10 m sample spacing. The
results of the edge detection between TSX and Sentinel-1 were incomparable. The detected
edges from TSX images were compared with edges mapped by GNSS. The obtained results
can't compete with automatic detection on optical images, however due to the SAR image
degradation by speckle noise, but the results are still acceptable for certain applications like
agriculture or forestry, monitoring large scale events such as earthquakes, floods or oil spill.
In addition SAR imagery carries more information that can be utilized besides object
detection in different applications such as radar interferometry or radar polarimetry.
52
Resume
V súčasnosti náš svet čelí zvýšenej hrozbe prírodných katastrof zapríčinených
klimatickými zmenami. Tieto zmeny sú podmienené prirodzeným alebo antropogénnym
pôvodom. Z toho dôvodu je v záujme mnohých svetových organizácií, ako je napr. Európska
vesmírna agentúra (ESA),
monitorovať
stav
životného
prostredia
na pevnine,
v oceánoch a v atmosfére. Cieľom je zaistiť dáta pre zistenie aktuálneho stavu životného
prostredia, predikciu vývoja klimatických zmien a tiež poskytnúť dáta pre krízový manažment
za účelom zvýšenia bezpečnosti obyvateľstva.
Účinným nástrojom na monitorovanie životného prostredia Zeme sa stal diaľkový
prieskum Zeme s využitím radaru so syntetickou apertúrou (SAR). Radar na rozdiel
od optických senzorov je aktívny senzor vybavený vlastným zdrojom mikrovlnného žiarenia.
Radar ožaruje oblasť záujmu a po interakcii žiarenia s cieľom zaznamenáva odrazené
žiarenie. Produktom spracovania tohto odrazeného signálu je 2D obraz snímanej oblasti.
Vlastný zdroj žiarenia a použitie vĺn z mikrovlnnej časti spektra umožňuje radaru
snímkovať zemský povrch cez deň, v noci a bez závislosti od počasia. Jediná radarová snímka
má tak potenciál zachytiť aktuálny stav ľadovcov alebo škody napáchané zemetrasením,
či lesnými požiarmi na území väčšom ako je 1000 km2. Dlhodobé kontinuálne snímkovanie
umožňuje
sledovanie
environmentálnych
zmien
a porovnaním
aktuálnych
snímok
s archivovanými snímkami sa dá zistiť napr. rozsah výrubu pralesov alebo nárast hladiny
oceánov.
Hlavným cieľom tejto práce je testovanie využitia družicových radarových snímok
na detekciu prvkov krajiny a ich zmien v čase. Hlavnou prekážkou je, že všetky radarové
snímky sú znehodnotené tzv. speckle šumom, ktorý vzniká interferenciou odrazeného
radarového signálu. Tento šum sťažuje akúkoľvek interpretáciu radarových snímok vrátane
detekcie objektov. Z tohto dôvodu musia byť pred extrakciou nových informácií radarové
snímky odšumené. K tomuto účelu práca obsahuje kvantitatívne porovnanie a zhodnotenie
účinnosti Frostovho filtra, Leeho filtra, vylepšeného Leeho filtra, Gamma MAP filtra
a vyhladzovacieho filtra založeného na Perona-Malikovej rovnici. Okrem jednotlivých filtrov
boli testované aj rôzne veľkosti másk filtrov a tiež aj viacero iterácií filtrovania.
Na posúdenie účinnosti filtrov bolo vybratých päť testovacích kritérií: ENL, SSI,
SMPI, EEI a FPI. ENL, SSI a SMPI indexy slúžili na zhodnotenie schopnosti redukovať
speckle šum na veľkých homogénnych oblastiach a pritom zachovávať rádiometrické
vlastnosti radarových snímok. EEI a FPI indexy slúžili na porovnanie schopnosti zachovávať
53
hrany. Manuálny výpočet pre každú snímku by bol zdĺhavý a s väčšou pravdepodobnosťou
omylu, preto bola navrhnutá jednoduchú funkciu Speckleeval, ktorá dokázala načítať spolu
s originálnou snímkou aj vyhladenú snímku, vypočítať všetky testovacie parametre a zapísať
ich do súboru XLS.
V zásade platilo, že s pribúdajúcim množstvom iterácií sa zlepšovala redukcia speckle
šumu, ale zhoršovala sa schopnosť zachovávania hrán. Najlepšie výsledky v redukcii speckle
šumu boli dosiahnuté Gamma MAP filtrom. Zároveň treba spomenúť, že po piatich iteráciách
filtrovania, boli výsledky Leeho a Frostovho filtra takmer totožné. Najlepšie zachovávanie
hrán podľa EEI a FPI indexov dosiahli Leeho vylepšený filter a Perona-Malikov filter.
Aplikovanie hranového detektora na vyhladené snímky prinieslo ďalšie výsledky. Detegované
hrany na snímkach vyhladených Gamma MAP filtrom boli oveľa hladšie a neprerušované ako
napr. pri Perona-Malikovom filtri. Z tohto dôvodu bol na odšumenie a pripravenie snímok na
detekciu hrán v záujmovom území zvolený Gamma MAP filter.
Záujmové územie je situované severovýchodne od hlavného mesta Slovenskej
republiky, Bratislavy. Záujmovú oblasť tvorí poľnohospodárske pole s rozlohou 219 hektárov.
Toto pole je na severovýchode ohraničené asfaltovou cestou, na severozápade stromoradím,
na juhozápade poľnou cestou a juhovýchodnú hranicu poľa tvorí stromoradie, poľná cesta
a ďalšie poľnohospodárske polia. Táto lokalita bola vybratá z dôvodu dobrého prístupu,
vhodným rôznorodým typom ohraničenia a zároveň veľkého množstva radarových snímok
z tohto územia.
Radarové dáta poskytnuté k experimentu pochádzali z misie TerraSAR-X (TSX)
a novej misie ESA, Sentinel-1. Snímky TerraSAR-X boli poskytnuté v rámci projektov
LAN1583 a LAN2833. Z projektu LAN1583 bola vybratá radarová snímka záujmového
územia zachytená 3. decembra 2008 s VV polarizáciou a so štvorcovým rozlíšením pixla
s rozmerom 0.75 m. Zo snímok určených pre projekt LAN 2833 boli vybraté dve snímky
z 21. januára 2013 a 2. júna 2013 s HH polarizáciou a rozlíšením pixla 1.25m
Dáta z družice Sentinel-1 sú voľne k dispozícií na portáli Sentinel-1 Scientific Data
Hub. Použitý produkt vo formáte GRD bol dostupný vo VV aj VH polarizácii so štvorcovým
rozlíšením pixla 10 m. Snímky boli zachytené 7. marca 2015.
Pre účel detekcie a extrakcie hrán bola vytvorená funkcia s názvom Saredge. Funkcia
dokáže načítať radarovú (alebo aj optickú) snímku vo formáte GeoTiff, vykonať detekciu
hrán s nastavením rôznych prahov, pretransformovať obrazové súradnice na mapové
súradnice za pomoci referencie uloženej v pôvodnej snímke a výsledok zapísať do textového
súboru. Detekcia hrán prebehla na všetkých uvedených radarových snímkach. Kritériom
54
bolo zaznamenať čo najviac skutočných hrán s súčasne minimalizovať detegované falošné
hrany.
Najlepšie výsledky boli dosiahnuté na snímke TSX z decembra 2008. Všetky dôležité
hrany boli zaznamenané s minimálnym počtom falošných hrán. Snímka z januára 2013
nepriniesla tak dobré výsledky. V dobe snímkovania bola záujmová oblasť zrejme
pod snehovou pokrývkou. Sneh je hladký povrch, preto sa väčšina žiarenia odrazila smerom
od radaru. Z toho dôvodu snímka obsahuje nízke amplitúdy (tmavé pixle) a nevýrazné hrany.
Detekcia hrán na snímke TSX z júna 2013 priniesla porovnateľné výsledky so snímkou
z decembra 2012. Porovnaním snímok bol zistené, že pôvodne jedno veľké poľnohospodárske
pole bolo rozdelené na dve menšie. Pokus detekcie na snímkach Sentinel-1 priniesol
najslabšie výsledky, ktoré nie sú porovnateľné so snímkami z družice TSX. Hlavným
dôvodom tohto neúspechu bolo nižšie rozlíšenie snímok.
Kvôli porovnaniu presnosti detegovaných hrán bolo v záujmovom území vykonané
meranie prístrojmi GNSS kinematickou metódou v reálnom čase. Jednotlivé hrany
pozostávali s veľkého množstva bodov. Body hrán, ktoré boli najbližšie k odmeraným bodom
prístrojmi GNSS boli vybraté a ich rozdiely medzi odmeranými bodmi boli vypočítané.
Rozdiely medzi GNSS meraním a detegovanými hranami boli prezentované vo forme grafov.
Z výsledkov je zrejmé, že táto metóda sa nemôže rovnať presnosti detekcie prvkov na
optických snímkach s vysokým rozlíšením, ale stále je použiteľná pre aplikáciu
v poľnohospodárstve alebo v lesníctve. Okrem amplitúdovej zložky, radarové dáta obsahujú
aj fázovú zložku, ktorá sa dá použiť v ďalších aplikáciách ako je napr. radarová
interferometreia, čo robí z technológie SAR užitočný nástroj pri sledovaní našej planéty.
55
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59
Appendices
60
Appendix A
Speckle Evaluation Results Part 1
Frost filter evaluation results
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
3Frost1
155.6345
47.8899
2293.4438
3.2263
0.6837
1.0052
0.8224
0.8988
3Frost2
155.5941
38.8406
1508.5917
4.9022
0.5546
0.8376
0.7014
0.8232
3Frost3
155.5833
33.5823
1127.7728
6.5567
0.4796
0.7294
0.6175
0.7713
3Frost4
155.5798
30.0370
902.2218
8.1954
0.4289
0.6538
0.5548
0.7130
3Frost5
155.5772
27.4432
753.1301
9.8175
0.3919
0.5984
0.5113
0.6675
5Frost1
155.4685
34.3807
1182.0294
6.2465
0.4913
0.8029
0.6214
0.7826
5Frost2
155.4273
25.6567
658.2652
11.2107
0.3668
0.6142
0.4756
0.6417
5Frost3
155.4191
21.4931
461.9534
15.9731
0.3073
0.5170
0.3956
0.5499
5Frost4
155.4156
18.9233
358.0923
20.6050
0.2705
0.4562
0.3476
0.4824
5Frost5
155.4123
17.1365
293.6583
25.1251
0.2450
0.4139
0.3242
0.4373
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
Lee filter evaluation results
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
3Lee1
156.0630
47.0906
2217.5234
3.3551
0.6704
0.7012
0.8180
0.9010
3Lee2
156.0611
38.3620
1471.6450
5.0555
0.5461
0.5723
0.6964
0.8267
3Lee3
156.0599
33.2633
1106.4455
6.7241
0.4736
0.4968
0.6139
0.7661
3Lee4
156.0575
29.8075
888.4841
8.3733
0.4244
0.4462
0.5520
0.7077
3Lee5
156.0562
27.2668
743.4802
10.0063
0.3882
0.4087
0.5077
0.6614
5Lee1
156.1017
32.4273
1051.5328
7.0790
0.4615
0.4650
0.5949
0.7610
5Lee2
156.0967
24.7935
614.7196
12.1085
0.3529
0.3573
0.4569
0.6167
5Lee3
156.0956
23.3398
544.7467
13.6636
0.3322
0.3367
0.4330
0.5821
5Lee4
156.0922
20.1087
404.3604
18.4066
0.2862
0.2911
0.3705
0.5127
5Lee5
156.0886
17.9803
323.2927
23.0211
0.2559
0.2612
0.3348
0.4556
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
61
Appendix B
Speckle Evaluation Results Part 1
Refined Lee filter evaluation results
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
ENL
SSI
EEI
FPI
Gamma MAP filter evaluation results
NAME
MEAN
STD
VAR
SMPI
3Gamma1
156.0265
47.1540
2223.4971
3.3446
0.6715
0.7267
0.8180
0.9010
3Gamma2
156.0201
38.4182
1475.9576
5.0381
0.5471
0.5956
0.6964
0.8267
3Gamma3
156.0171
33.3139
1109.8189
6.6999
0.4744
0.5179
0.6139
0.7661
3Gamma4
156.0132
29.8555
891.3493
8.3417
0.4252
0.4657
0.5520
0.7077
3Gamma5
156.0103
27.3142
746.0678
9.9657
0.3890
0.4272
0.5077
0.6614
5Gamma1
156.0998
32.4315
1051.8019
7.0770
0.4616
0.4660
0.5949
0.7610
5Gamma2
156.0946
24.7967
614.8767
12.1051
0.3529
0.3581
0.4569
0.6167
5Gamma3
156.0913
20.9949
440.7865
16.8853
0.2988
0.3042
0.3881
0.5345
5Gamma4
156.0892
18.5933
345.7098
21.5284
0.2647
0.2700
0.3426
0.4679
5Gamma5
156.0874
16.8969
285.5053
26.0676
0.2405
0.2457
0.3226
0.4325
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
Perona-Malik filter evaluation results
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
62
Appendix C
Speckle Evaluation Results Sorted by ENL
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
original
156.1093
70.2631
4936.9075
1.5079
-
-
-
-
The higher ENL, the better speckle reduction.
63
Appendix D
Speckle Evaluation Results Sorted by SSI
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
original
156.1093
70.2631
4936.9075
1.5079
-
-
-
-
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
The lower SSI, the better speckle reduction.
64
Appendix E
Speckle Evaluation Results Sorted by SMPI
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
original
156.1093
70.2631
4936.9075
1.5079
-
-
-
-
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
The lower SMPI, the better speckle reduction.
65
Appendix F
Speckle Evaluation Results Sorted by EEI
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
original
156.1093
70.2631
4936.9075
1.5079
-
-
-
-
The higher EEI, the better edge preservation.
66
Appendix G
Speckle Evaluation Results Sorted by FPI
NAME
MEAN
STD
VAR
ENL
SSI
SMPI
EEI
FPI
PM_1
153.3511
52.3490
2740.4207
2.6214
0.7584
2.8000
0.7140
0.8638
RefLee1
157.3106
43.6266
1903.2788
3.9718
0.6162
1.3668
0.6844
0.8410
RefLee2
157.5995
34.6859
1203.1088
6.3064
0.4890
1.2293
0.5682
0.7688
7Frost1
155.6699
27.0595
732.2166
10.1099
0.3862
0.5544
0.5040
0.7074
RefLee3
157.5961
27.1274
735.8940
10.3099
0.3824
0.9601
0.4536
0.6847
PM_2
151.7369
37.9394
1439.3985
4.8863
0.5555
2.9009
0.5489
0.6711
RefLee4
157.5578
25.0301
626.5037
12.1042
0.3530
0.8722
0.4212
0.6486
RefLee5
157.5211
23.4333
549.1184
13.8035
0.3305
0.8043
0.3978
0.6228
7Lee1
156.0987
24.5578
603.0831
12.3424
0.3495
0.3532
0.4371
0.6141
7Gamma1
156.0987
24.5578
603.0842
12.3424
0.3495
0.3532
0.4371
0.6141
PM_3
150.8968
27.5230
757.5182
9.1822
0.4052
2.4335
0.4123
0.5443
7Frost2
155.6372
19.2665
371.1975
19.9343
0.2750
0.4037
0.3557
0.5053
PM_4
150.5074
20.8780
435.8890
15.8752
0.3082
1.9617
0.3357
0.4635
7Lee2
156.0931
18.3072
335.1531
22.2077
0.2606
0.2648
0.3348
0.4572
7Gamma2
156.0931
18.3072
335.1548
22.2075
0.2606
0.2648
0.3348
0.4572
7Frost3
155.6294
15.9727
255.1257
29.0007
0.2280
0.3364
0.3195
0.4136
PM_5
150.3294
16.9216
286.3391
24.1094
0.2501
1.6328
0.3005
0.4045
7Lee3
156.0882
15.4482
238.6475
31.1862
0.2199
0.2245
0.3122
0.3954
7Gamma3
156.0882
15.4482
238.6478
31.1862
0.2199
0.2245
0.3122
0.3954
7Frost4
155.6248
14.0116
196.3238
37.6846
0.2000
0.2960
0.3095
0.3670
7Lee4
156.0851
13.6735
186.9637
39.8056
0.1946
0.1993
0.3064
0.3615
7Gamma4
156.0851
13.6735
186.9640
39.8055
0.1946
0.1993
0.3064
0.3615
7Frost5
155.6221
12.6711
160.5576
46.0777
0.1809
0.2682
0.3064
0.3433
7Gamma5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
7Lee5
156.0827
12.4316
154.5449
48.1541
0.1770
0.1816
0.3036
0.3282
The higher FPI, the better edge preservation.
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