Research Papers

Object oriented method for detection of inundation extent using multi-polarized synthetic aperture radar image

[+] Author Affiliations
Guozhuang Shen

and Graduate University of Chinese Academy of Science, State Key Laboratory of Remote Sensing Science, Datun Road, Beijing, Beijing, Beijing 100101 China

Huadong Guo, Jingjuan Liao

State Key Laboratory of Remote Sensing Science, Datun Road, Beijing, Beijing, Beijing 100101 China

J. Appl. Remote Sens. 2(1), 023512 (March 28, 2008). doi:10.1117/1.2911669
History: Received June 27, 2007; Revised March 18, 2008; Accepted March 20, 2008; March 28, 2008; Online March 28, 2008
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Abstract

With the all-weather and day/night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent detection. The inundation area detection using SAR will be easy as a result of the dark image tones yielded by specular reflection to the radar wave. Object oriented method (OOM) was applied to detect inundation extent using multi-polarized ENVISAT ASAR data. The traditional pixel-based methods used in information extraction and classification focus on the single pixel, so when they are applied in SAR imagery no perfect results can be achieved because of the speckle of SAR imagery. On the other hand, the pixel-based methods have limitations for detecting inundation extent and flood monitoring because of the neglect of the information of the adjacent pixels. The OOM, which no longer looks at individual pixels, but rather homogeneity areas (image objects), would be much more effective. In this paper, the OOM is applied in the ENVISAT ASAR alternative polarized (VV/VH) images using the software eCognition. The study site is located in Poyang Lake wetland, which has different inundation extent at different time. The images were segmented firstly, then the standard nearest neighbor classifier and the membership function classifier were used to classify the image objects, finally the different inundation areas were detected. The classification accuracies for two classifiers from the OOM are 95.78% and 92.24%, which are higher than that of the maximum likelihood classifier, 86.02%.

© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

Guozhuang Shen ; Huadong Guo and Jingjuan Liao
"Object oriented method for detection of inundation extent using multi-polarized synthetic aperture radar image", J. Appl. Remote Sens. 2(1), 023512 (March 28, 2008). ; http://dx.doi.org/10.1117/1.2911669


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