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Proceedings Paper

Histogram-based segmentation for stationary target detection in urban environments
Author(s): Moeness G. Amin; Pawan Setlur; Fauzia Ahmad; Pascale Sevigny; David DiFilippo
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Paper Abstract

Detection of stationary targets in urban sensing and through-the-wall radar images using likelihood ratio test (LRT) detectors has recently been considered in the literature. A shortcoming of the LRT detectors is that appropriate probability density functions of target and clutter images need to be predefined. In most practical scenarios, this information is not available a priori, and the mismatch of the assumed distribution functions degrades the performance of the LRT. In this paper, we apply image segmentation techniques to radar images of scenes associated with urban sensing. More specifically, the Otsu's method and maximum entropy segmentation are considered to aid in removing the clutter, resulting in enhanced radar images with target regions only. Performance of the segmentation schemes is evaluated and compared to that of the assumed LRT detector using real-data collected with Defence Research and Development Canada's vehicle-borne through-the-wall radar imaging system. The results show that, although the principles of segmentation and detection are different and serve disparate objectives, the segmentation techniques outperform the LRT detector for the considered cases.

Paper Details

Date Published: 3 May 2012
PDF: 7 pages
Proc. SPIE 8361, Radar Sensor Technology XVI, 83610M (3 May 2012); doi: 10.1117/12.919687
Show Author Affiliations
Moeness G. Amin, Villanova Univ. (United States)
Pawan Setlur, Univ. of Illinois at Chicago (United States)
Fauzia Ahmad, Villanova Univ. (United States)
Pascale Sevigny, Defence Research and Development Canada, Ottawa (Canada)
David DiFilippo, Defence Research and Development Canada, Ottawa (Canada)

Published in SPIE Proceedings Vol. 8361:
Radar Sensor Technology XVI
Kenneth I. Ranney; Armin W. Doerry, Editor(s)

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