Multiscale saliency detection method for ship targets in synthetic aperture radar images

Abstract

Ship detection is an important direction of synthetic aperture radar (SAR) image application in maritime surveillance. A multi-scale optimisation threshold saliency detection is developed in this letter, which is used to detect ships targets of SAR images. The SAR image is first decomposed into a pyramid image sequence. Then the saliency detection is performed using the spectral residual method for each layer in the sequence, and the salient sub-graph that contains ship targets is obtained. Finally, each sub-graph is fused and the optimisation threshold segmentation method that applies to the saliency map gives the final detection result. Experimental results show that the proposed approach has low complexity and high detection accuracy and greatly reduces the dependence on prior knowledge.

Keywords

• object detection
• image segmentation
• ships
• radar imaging
• synthetic aperture radar
• image sequences
• radar detection
• marine radar
• optimisation
• graph theory
• image fusion
• multiscale saliency detection method
• synthetic aperture radar image application
• maritime surveillance
• multiscale optimisation threshold saliency detection
• pyramid image sequence
• spectral residual method
• optimisation threshold segmentation method
• ship target detection
• sar image application