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Journal of Applied Remote Sensing

Change detection methods for identification of mines in synthetic aperture radar imagery
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Paper Abstract

This paper presents several methods for change detection in a pair of multi-temporal synthetic aperture radar (SAR) images of the same scene. Several techniques which vary in complexity were implemented and compared. Among the simple methods that were implemented are differencing, Euclidean distance, and image mean ratioing. These methods require minimal processing time, with little computational complexity, and incorporate no statistical information. These methods have demonstrated some degree of accuracy in detecting changes in SAR imagery. However, the presence of highly correlated speckle noise, misregistration errors, and nonlinear variations in SAR images motivated us to seek more sophisticated methods of change detection in order to obtain more favorable results. Therefore, methods were implemented which incorporated second order statistic calculations in making a change decision in efforts to mitigate false alarms arising from the aforementioned causes. Pre-whitened the data was created and then a Wiener prediction-based method, Euclidean distance measure and subspace projection method was implemented. The performance of these methods were compared using multi-look SAR images containing several targets (mines). The results are presented in the form of receiver operating characteristics (ROC) curves.

Paper Details

Date Published: 1 August 2007
PDF: 15 pages
J. Appl. Rem. Sens. 1(1) 013525 doi: 10.1117/1.2776947
Published in: Journal of Applied Remote Sensing Volume 1, Issue 1
Show Author Affiliations
Nasser M. Nasrabadi, Army Research Lab. (United States)
Maria G. Tates, Army Research Lab. (United States)
Heesung Kwon, Johns Hopkins Univ. (United States)

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