Research Papers

On the application of a spatial chaotic model for detecting landcover changes in synthetic aperture radar images

[+] Author Affiliations
Nien-Shiang Chou

Department of Information Engineering, National United University, Maio-Li, Taiwan

Yu-Chang Tzeng

Department of Electronic Engineering, National United University, Maio-Li, Taiwan

Kun Shan Chen

Institute of Computer Science and Information Engineering, National Central University, 300 Jung-Da Rd., Chung-LI, Taiwan 32054 Taiwan

Chih-Tien Wang

Center for Space and Remote Sensing Research, National Central University, Chung-Li, Taiwan

Kuo-Chin Fan

Institute of Computer Science and Information Engineering, National Central University, 300 Jung-Da Rd., Chung-Li, Taiwan 32001 Taiwan

J. Appl. Remote Sens. 3(1), 033512 (February 23, 2009). doi:10.1117/1.3098431
History: Received February 26, 2008; Revised January 21, 2009; Accepted February 7, 2009; February 23, 2009; Online February 23, 2009
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Abstract

We present a change detection method for terrain covers from multi-temporal SAR images based on a spatial chaotic model which is known to adequately characterize the coherent process of SAR imaging. The major problem of SAR change detection rises from both the presence of speckle noise and the pixel mis-registration that are commonly seen in the remote sensing image. By means of chaotic model, we first transform the images to fractal domain and then perform the CFAR detection. Simulated tests are conducted to quantitatively evaluate the impacts of these two major error sources on detection rate. Results from satellite SAR for landcover change detection clearly show that the proposed algorithm not only the speckle noise can be effectively suppressed without scarifying the spatial resolution; the excruciating mis-registration error was taken into account and removed.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Nien-Shiang Chou ; Yu-Chang Tzeng ; Kun Shan Chen ; Chih-Tien Wang and Kuo-Chin Fan
"On the application of a spatial chaotic model for detecting landcover changes in synthetic aperture radar images", J. Appl. Remote Sens. 3(1), 033512 (February 23, 2009). ; http://dx.doi.org/10.1117/1.3098431


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