Object Detection in Single SAR Images via a Saliency Framework Integrating Bayesian Inference and Adaptive Iteration
Haixiang Li, Haohao Ren, Yun Zhou, Lin Zou, Xuegang Wang · Remote Sensing · 2025
Object detection in single synthetic aperture radar (SAR) imagery has always been essential for SAR interpretation. Over the years, the saliency-based detection method is considered as a strategy that can overcome some inherent deficiencies in traditional SAR detection and arouses widespread attention. Considering that the conventional saliency method usually suffers performance loss in saliency map generation from lacking specific task priors or highlighted non-object regions, this paper is devoted to achieving excellent salient object detection in single SAR imagery via a two-channel framework integrating Bayesian inference and adaptive iteration. Our algorithm firstly utilizes the two processing channels to calculate the object/background prior without specific task information and extract four typical features that can enhance the object presence, respectively. Then, these two channels are fused to generate an initial saliency map by Bayesian inference, in which object areas are assigned with high saliency values. After that, we develop an adaptive iteration mechanism to further modify the saliency map, during which object saliency is progressively enhanced while the background is continuously suppressed. Thus, in the final saliency map, there will be a distinct difference between object components and the background, allowing object detection to be realized easily by global threshold segmentation. Extensive experiments on real SAR images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and SAR Ship Detection Dataset (SSDD) qualitatively and quantitatively demonstrate that our saliency map is superior to those of four classical benchmark methods, and final detection results of the proposed algorithm present better performance than several comparative methods across both ground and maritime scenarios.