Target detection in SAR: Parallel algorithms, context extraction, and region adaptive techniques

This paper summarizes some recent results in several areas of SAR target detection. We present detection algorithms designed for parallel computation and a multiresolution segmentation algorithm used to extract the context of target detections and adapt the detection algorithms to the regions being processed. We consider cell averaging and order statistic constant false alarm rate (CFAR) detectors with a Weibull clutter background on SIMD linear arrays with simple fixed point arithmetic units. After considering the parallel computation of the various CFAR algorithms, we focus on the problem of segmenting the SAR imagery to obtain the context of the detections and to adapt the detector parameters for the area under test, Our multiresolution segmentation uses maximum likelihood estimation possibly followed by the iterative conditional modes (ICM) algorithm at each resolution with merging accomplished using confidence levels. The algorithms are tested on 1ft resolution SAR intensity images taken from the TESAR sensor onboard the Predator unmanned aerial vehicle (UAV).