Target detection by change for SAR imagery

Chris J. Willis

doi:10.1117/12.898405

26 October 2011 Target detection by change for SAR imagery

Chris J. Willis

Proceedings Volume 8179, SAR Image Analysis, Modeling, and Techniques XI; 81790O (2011) https://doi.org/10.1117/12.898405
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

Change detection provides a powerful means for the initial detection of small target objects of interest. However, speckle effects mean this type of approach can be difficult to apply to Synthetic Aperture Radar (SAR) imagery. This paper examines methods for object detection using change between a registered pair of SAR images. The techniques discussed are designed to detect change over small areas ranging in size from a few to perhaps a few hundred pixels. The techniques considered include the ratio of pixels and the ratio of variances covering small regions. The former is a straightforward approach and can provide a good performance baseline. The latter utilises the observation that many man-made objects have a somewhat spiky scattering response, the variance tends to capture this type of response and the ratio of variance enables comparison. Ideally any test statistic should be characterized by a known statistical distribution such that formal tests of a null hypothesis might be carried out. Here the null hypothesis corresponds to no change, and knowledge of the distribution of the test statistic enables the implementation of a Constant False-Alarm Rate (CFAR) detection process. The analysis carried out herein considers the distribution of the ratio statistics under realistic operating parameterisations for target detection in SAR imagery. Results are presented for a registered image pair in the form of detection maps. The simple ratio is found to be considerably more sensitive to image speckle than techniques covering small regions in the imagery.

Citation Download Citation

Chris J. Willis "Target detection by change for SAR imagery", Proc. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, 81790O (26 October 2011); https://doi.org/10.1117/12.898405

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