Target detection and classification in SAR images using region covariance and co-difference
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/26739
Proceedings of SPIE - The International Society for Optical Engineering
- Conference Paper 
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.
Showing items related by title, author, creator and subject.
Duman, K.; Çetin, A.E. (2010)Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search ...
Duman, Kaan (Bilkent University, 2009)Automatic recognition and classification of man-made objects in SAR (Synthetic Aperture Radar) images have been an active research area because SAR sensors can produce images of scenes in all weather conditions at any ...
A particle swarm optimization based SAR motion compensation algorithm for target image reconstruction Ugur, S.; Arikan, O. (2010)A new SAR motion compensation algorithm is proposed for robust reconstruction of target images even under large deviations of the platform from intended flight path. Phase error due to flight path deviations is estimated ...