Frequency dependence of ATD performance in foliage-penetrating SAR images

Target detection in foliage-penetrating (FOPEN), ultrawideband synthetic aperture radar (UWB SAR) images is a challenging problem. Given the low signal-to-clutter ratio, conventional detection algorithms perform poorly in FOPEN SAR images. Using symmetric alpha-stable (SαS) densities to model the impulsive noise, we have developed a region-adaptive automatic target detection (ATD) algorithm. The image is first segmented, and the resulting labeled image is exploited by a region-adaptive target detection algorithm. We evaluate the performance of the algorithm in different frequency bands, and determine which subbands are useful for image segmentation and target detection