LAUSR

The increasing resolution of synthetic aperture radar (SAR) images makes ship targets appear to be more structured and shaped and nevertheless contain many weak echoes in their resolution cells, which brings great challenges for accurate scene understanding. In this paper, inspired by the multilayer selective cognition property of the human visual system, we advance a new hierarchical saliency filtering method for fast and accurate ship detection in high-resolution SAR images. The saliency of targets is first explored to develop a random-forest-based hierarchical sparse model (HSM) for the selection of candidate target regions. Then, a dynamic constant-false-alarm-rate-based contour saliency model (CSM) is proposed to gradually filter out the false alarms from candidate regions and extract the target outlines for accurate detection. Because of a rapid capture of regions of interest in the HSM and dynamic false alarm removal in the CSM, our method can make efficient ship detection in high-resolution SAR images possible by working in a coarse-to-fine manner. Finally, the proposed ship detector is tested on high-resolution SAR data collected from TerraSAR and RADARSAT satellites, showing significant agreement with the ground truth. It is also compared with other classical ship detectors, in terms of both speed and accuracy, and shows superior performance, particularly in complex scenes.