Target detection and classification in SAR images using region covariance and co-difference

Date
2009-04
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Source Title
Proceedings of SPIE - Algorithms for Synthetic Aperture Radar Imagery XVI
Print ISSN
0277-786X
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Publisher
SPIE
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Language
English
Type
Conference Paper
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Abstract

In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a covariance matrix whose entries are used in target detection and classification. In addition the region co-difference matrix is also introduced. Experimental results of object detection in MSTAR (moving and stationary target recognition) database are presented. The RC and region co-difference method delivers high detection accuracy and low false alarm rates. It is also experimentally observed that these methods produce better results than the commonly used principal component analysis (PCA) method when they are used with different distance metrics introduced. © 2009 SPIE.

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Keywords
Automatic target recognition (ATR) and classification, Principal component analysis (PCA), Region co-difference matrix, Region covariance (RC), Synthetic aperture radar (SAR) images, Detection accuracy, Difference matrix, Difference method, Distance metrics, False alarm rate, Feature parameters, New approaches, Object Detection, SAR Images, Stationary targets, Synthetic aperture radar images, Target detection, Automatic target recognition, Covariance matrix, Feature extraction, Image classification, Imaging systems, Object recognition, Parameter extraction, Photoacoustic effect, Principal component analysis, Radar, Radar antennas, Radar imaging, Synthetic aperture radar, Synthetic apertures, Target drones, Target tracking, Targets, Tracking radar, Radar target recognition
Citation
Published Version (Please cite this version)