1. ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng

2. Table of Contents Introduction The Spatial Chaotic Model Experimental Results Conclusions

3. 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.

4. 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.

5. 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.

6. The Spatial Chaotic Model (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.

7. The Spatial Chaotic Model (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.

8. 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.

9. Fractal Dimension (2) Then, fractal dimension D is estimated by least squares of log(N r ) against log(1/r) through a linear equation

10. Differential Box Counting (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 g l and g u, respectively.

11. Differential Box Counting (2) The number of boxes at grid (k,l) is The total number of boxes in the whole region of interest is

12. Differential Box Counting (3) For n different values of r, the fractal dimension D and offset c can be computed by where and y=log(N r ) and x=log(1/r)

13. The Test Site The 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.

14. Location of the Test Site Wazihwei Nature Reserve

15. An Optical Image of the Test Site Google Earth (July 31, 2006) mud flats marshes

16. An SAR Image of the Test Site TerraSAR X-band and HH polarization (May 15, 2008)

17. Histogram of the Normalized SAR Scattering Coefficient

18. A Fractal Image of the Test Site

19. Histogram of the Fractal Dimension

20. Estuary Wetland Detection Mud 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.

21. Thresholding Marshes Mud Flats

22. Detected Image Detection Procedures Thresholding SAR Image Edge Detection Filtering Filling SCM

23. Detection Results

24. A Close Look of the Detected Wetland

25. Conclusions Experimental 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.