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Journal of Applied Remote Sensing

Classification of levee slides from airborne synthetic aperture radar images with efficient spatial feature extraction
Author(s): Deok Han; Qian Du; James V. Aanstoos; Nicolas Younan
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Paper 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.

Paper Details

Date Published: 27 March 2015
PDF: 10 pages
J. Appl. Remote Sens. 9(1) 097294 doi: 10.1117/1.JRS.9.097294
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Deok Han, Mississippi State Univ. (United States)
Qian Du, Mississippi State Univ. (United States)
James V. Aanstoos, Mississippi State Univ. (United States)
Nicolas Younan, Mississippi State Univ. (United States)


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