Special Section on Management and Analytics of Remotely Sensed Big Data

Classification of levee slides from airborne synthetic aperture radar images with efficient spatial feature extraction

[+] Author Affiliations
Deok Han, Qian Du, Nicolas Younan

Mississippi State University, Department of Electrical and Computer Engineering, Starkville, Mississippi 39762, United States

James V. Aanstoos

Mississippi State University, Geosystems Research Institute, Starkville, Mississippi 39762, United States

J. Appl. Remote Sens. 9(1), 097294 (Mar 27, 2015). doi:10.1117/1.JRS.9.097294
History: Received December 31, 2014; Accepted March 11, 2015
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Abstract.  Levee slides may result in catastrophic damage to the region of failure. Remote sensing data, such as synthetic aperture radar (SAR) images, can be useful in levee monitoring. Because of the long length of a levee, the image size may become too large to use computationally expensive methods for quick levee monitoring, so time-efficient approaches are preferred. The popular support vector machine classifier does not work well on the original three polarized SAR magnitude bands without spatial feature extraction. Gray-level co-occurrence matrix is one of the most common methods for extracting textural information from gray-scale images, but it may not be practically useful for a big data in terms of calculation time. In this study, very efficient feature extraction methods with spatial low-pass filtering are proposed, including a weighted average filter and a majority filter in conjunction with a nonlinear band normalization process. Experimental results demonstrated that these filters can provide comparable results with much lower computational cost.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Deok Han ; Qian Du ; James V. Aanstoos and Nicolas Younan
"Classification of levee slides from airborne synthetic aperture radar images with efficient spatial feature extraction", J. Appl. Remote Sens. 9(1), 097294 (Mar 27, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.097294


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