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ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.
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Transcript of ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.
![Page 1: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/1.jpg)
ESTUARY WETLAND ESTUARY WETLAND DETECTION IN SAR IMAGESDETECTION IN SAR IMAGES
PresentedBy
Yu-Chang Tzeng
![Page 2: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/2.jpg)
Table of ContentsTable of ContentsIntroductionThe Spatial Chaotic Model Experimental ResultsConclusions
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Introduction (1)Introduction (1)Wetland detection is an important
subject from the viewpoint of conservation of ecosystem and wetland change.
Features reflecting the roughness of an image can be very useful for detecting estuary wetland.
SAR images are particularly effective to detect estuary wetlands because they provide information on surface roughness.
![Page 4: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/4.jpg)
Introduction (2)Introduction (2)However, estuary wetland detection
in SAR images suffers from the presence of speckle effect.
Image despeckling process leads to loss of the geometrical details to some extent which subsequently degrades the detection performance.
When speckle has been modeled properly, the image despeckling process is no longer required.
![Page 5: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/5.jpg)
Introduction (3)Introduction (3)To represent its geometric
property, an SAR signal can be modeled by the spatial chaotic model (SCM).
SCM was adopted to detect estuary wetland in SAR images.
![Page 6: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/6.jpg)
The Spatial Chaotic Model The Spatial Chaotic Model (1)(1)SAR signals can be treated as the state
variables of a nonlinear dynamical system
where the nonlinear function F is a vector.It is possible to construct a correlation
function, C(m,r) as
where m is an integer, and H(·) is the Heaviside function.
K
n
mK
njjjnrH
KKrmC
1
1
,1111
, xx
nnn xFxx 1
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The Spatial Chaotic Model The Spatial Chaotic Model (2)(2)The number of data points K is
assumed to be large and the limiting behavior of C(m,r) for small r is described by
where D, called fractal dimension, is assumed to exist.
As a result, SAR signal can be characterized by its fractal dimension.
Dmrm,rC 1
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Fractal Dimension (1)Fractal Dimension (1)The fractal dimension D is defined
to be the number that satisfies
where r is the side length of the boxes, Nr is the number of boxes needed to contain all the points of the geometric objects, and C is a proportionality constant.
D
rr CrN
0lim
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Fractal Dimension (2)Fractal Dimension (2)Then, fractal dimension D is
estimated by least squares of log(Nr) against log(1/r) through a linear equation crDNr /1loglog
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Differential Box Counting Differential Box Counting (1)(1)A sub-image of window size M
centered at pixel (i,j) is grouped. The window is further partitioned
into several grids. Each grid is of size s, where M / 2 ≧
s > 1 and r = s / M is estimated. At grid (k,l), let the minimum and
maximum gray levels of the image in this grid be gl and gu, respectively.
![Page 11: ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.](https://reader036.fdocuments.us/reader036/viewer/2022062517/56649f315503460f94c4ce15/html5/thumbnails/11.jpg)
Differential Box Counting Differential Box Counting (2)(2)The number of boxes at grid (k,l)
is
The total number of boxes in the whole region of interest is
1, lur gglkn
lk
rr lknN,
,
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Differential Box Counting Differential Box Counting (3)(3)For n different values of r, the
fractal dimension D and offset c can be computed by
where and y=log(Nr) and x=log(1/r)
and ,2
11
2
1 11
n
ii
n
ii
n
i
n
iii
n
iii
xxn
yxyxnD
n
ii
n
ii x
nDy
nc
11
11
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The Test SiteThe Test SiteThe test site is located at Wazihwei
Nature Reserve, on southern side of Danshui River, Ba-li Village, Taipei County, Taiwan.
The test site is separated into sandy beach and coastal wetland.
Swampland is formed because mangroves carry along with lots of sand organic materials from Danshui River.
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Location of the Test SiteLocation of the Test Site
Wazihwei Nature Reserve
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An Optical Image of the An Optical Image of the Test SiteTest Site
Google Earth (July 31, 2006)
mud flats
marshes
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An SAR Image of the Test An SAR Image of the Test SiteSite
TerraSAR X-band and HH polarization (May 15, 2008)
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Histogram of the Histogram of the Normalized Normalized SAR Scattering CoefficientSAR Scattering Coefficient
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A Fractal Image of the A Fractal Image of the Test SiteTest Site
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Histogram of the Fractal Histogram of the Fractal DimensionDimension
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Estuary Wetland Estuary Wetland DetectionDetectionMud flats bear lower fractal
dimensions than those of ocean and land areas.
Marshes have fractal dimensions in between those of ocean and land areas.
The detection of estuary wetland is carried out by a simple thresholding of the cumulative histogram of the fractal image for a predefined CFAR value.
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ThresholdingThresholding
MarshesMud Flats
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Detected Image
Detection ProceduresDetection Procedures
Thresholding
SAR Image
Edge Detection
Filtering
Filling
SCM
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Detection ResultsDetection Results
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A Close Look of the Detected A Close Look of the Detected WetlandWetland
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ConclusionsConclusionsExperimental results indicated
that mud flats are detectable. Preliminary results supported the
effectiveness and superior performance of the proposed method.
Further study for the detection of marshes is still under investigation.