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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
Adaptive target detection in foliage-penetrating SAR images using alpha-stable models
A Banerjee1, P Burlina, R Chellappa
1Center for Automation Research, Department of Electrical Engineering, University of Maryland, College Park, MD 20742-3275, USA. banerjee@cfar.umd.edu
Abstract:
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 (SalphaS) 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 SalphaS 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.