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

Image segmentation

Synthetic aperture radar

Image classification


Fuzzy logic


Classification systems

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