Histogram-based segmentation for stationary target detection in urban environments

Moeness G. Amin; Pawan Setlur; Fauzia Ahmad; Pascale Sevigny; David DiFilippo

doi:10.1117/12.919687

3 May 2012 Histogram-based segmentation for stationary target detection in urban environments

Moeness G. Amin, Pawan Setlur, Fauzia Ahmad, Pascale Sevigny, David DiFilippo

Proceedings Volume 8361, Radar Sensor Technology XVI; 83610M (2012) https://doi.org/10.1117/12.919687
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Moeness G. Amin, Pawan Setlur, Fauzia Ahmad, Pascale Sevigny, and David DiFilippo "Histogram-based segmentation for stationary target detection in urban environments", Proc. SPIE 8361, Radar Sensor Technology XVI, 83610M (3 May 2012); https://doi.org/10.1117/12.919687

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available