Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

✍ Scribed by Tristrom Cooke; Nicholas J. Redding; Jim Schroeder; Jingxin Zhang

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 148 KB
• Volume: 10
• Category: Article
• ISSN: 1051-2004
• DOI: 10.1006/dspr.2000.0379

✦ Synopsis

Several methods are available that capture the statistics of radar imagery. The best features, in the sense of man-made target discrimination, are expected to be different for different types of natural background and for different objects of interest such as vehicles. We demonstrate that discrimination of natural background and man-made objects using low resolution synthetic aperture radar imagery is possible using singular value decomposition; several other simple features are also used to augment the feature vector. We use a subset of eigenvectors as features for target discrimination. The optimal set of features used to classify a region as "background clutter only" or "target region" is automatically chosen by a standard suboptimal feature selection algorithm.