Adaptive target detection in foliage-penetrating SAR images using alpha-stable models

Detecting targets occluded by foliage in foliage-penetrating (FOPEN) ultra-wideband synthetic aperture radar (UWB SAR) images is an important and challenging problem. Given the different nature of target returns in foliage and nonfoliage regions and very low signal-to-clutter ratio in UWB imagery, conventional detection algorithms fail to yield robust target detection results. A new target detection algorithm is proposed that 1) incorporates symmetric alpha-stable (S alpha S) distributions for accurate clutter modeling, 2) constructs a two-dimensional (2-D) site model for deriving local context, and 3) exploits the site model for region-adaptive target detection. Theoretical and empirical evidence is given to support the use of the S alpha S model for image segmentation and constant false alarm rate (CFAR) detection. Results of our algorithm on real FOPEN images collected by the Army Research Laboratory are provided.