ESTUARY WETLAND ESTUARY WETLAND DETECTION IN SAR IMAGESDETECTION IN SAR IMAGES

PresentedBy

Yu-Chang Tzeng

Table of ContentsTable of ContentsIntroductionThe Spatial Chaotic Model Experimental ResultsConclusions

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.

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.

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.

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

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

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

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

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.

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,

,

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

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.

Location of the Test SiteLocation of the Test Site

Wazihwei Nature Reserve

An Optical Image of the An Optical Image of the Test SiteTest Site

Google Earth (July 31, 2006)

mud flats

marshes

An SAR Image of the Test An SAR Image of the Test SiteSite

TerraSAR X-band and HH polarization (May 15, 2008)

Histogram of the Histogram of the Normalized Normalized SAR Scattering CoefficientSAR Scattering Coefficient

A Fractal Image of the A Fractal Image of the Test SiteTest Site

Histogram of the Fractal Histogram of the Fractal DimensionDimension

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.

ThresholdingThresholding

MarshesMud Flats

Detected Image

Detection ProceduresDetection Procedures

Thresholding

SAR Image

Edge Detection

Filtering

Filling

SCM

Detection ResultsDetection Results

A Close Look of the Detected A Close Look of the Detected WetlandWetland

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.