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

Kaan Duman; Abdulkadir Eryildirim; A. Enis Cetin

doi:10.1117/12.818725

28 April 2009 Target detection and classification in SAR images using region covariance and co-difference

Kaan Duman, Abdulkadir Eryildirim, A. Enis Cetin

Proceedings Volume 7337, Algorithms for Synthetic Aperture Radar Imagery XVI; 73370P (2009) https://doi.org/10.1117/12.818725
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Kaan Duman, Abdulkadir Eryildirim, and A. Enis Cetin "Target detection and classification in SAR images using region covariance and co-difference", Proc. SPIE 7337, Algorithms for Synthetic Aperture Radar Imagery XVI, 73370P (28 April 2009); https://doi.org/10.1117/12.818725

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