Context-Driven Ship, Vehicle, and Aircraft Detection in Colored Synthetic Aperture Radar (SAR) Images.

In slow-time colorized subaperture image (CSI), anisotropic targets that reflect strongly when viewed from specific angles appear in vivid colors, which makes them stand out against isotropic background that reflects energy uniformly across all angles. It leads to more accurate annotation labels for ships, vehicles, and airplanes in SAR images and better SAR automatic target detection (ATD) performance. Unfortunately, although many port-related CSI products collected by satellite-borne SAR systems are released for free public access and could be leveraged for ship detection research, those that could support vehicle and airplane detection are rare. To investigate performance improvement in deep learning-based SAR ATD that could be brought by colored SAR images, three novel SAR-ATD frameworks are proposed for ship, vehicle, and aircraft detection, respectively. (1) Context-guided ensemble learning (CGEL) is proposed for ship detection, where state-of-the-art high-resolution colorized spotlight SAR images are exploited to enhance the visual features of ships and reduce false alarms, while the potential ship berthing/docking areas are delimited with adaptive intensity shading (AIS). (2) Context-driven SAR image recoloring and enhancement mechanism (CD-SAR-REM) is proposed to generate a context-driven color-enhanced version of the original SAR image based on AIS so that potential parking regions are highlighted. (3) Color feature-aided aircraft detection. In case that CSI products are unavailable, pseudo-color SAR images are generated based on phase congruency and the contextual information extracted by the segmentation module is used to refine the initial predictions generated by the core detection network. Experimental results show that the performance of the proposed context-driven ship, vehicle, and aircraft detection methods based on colored SAR images are superior to many state-of-the-art SAR ATD models.