Ship Detection in Synthetic Aperture Radar Imagery Based on Discriminative Dictionary Learning

The ship target detection technology based on SAR image has important significance in military and civil field applications and is one of the research hotspots at this stage. In this paper, the research work on typical problems in SAR image ship target detection is carried out. A ship target detection algorithm based on discriminative dictionary learning is proposed, which mainly includes image denoising, candidate region extraction and candidate region identification. Firstly, an adaptive non-local filtering method is used to denoise the SAR image. Then the gradient feature map reconstruction algorithm is used to extract the candidate regions. Finally, the category constrained discriminative dictionary learning method is used to classify the candidate regions. The algorithm is based on GF-3 and Terra SAR data. The experimental results show that the proposed algorithm has strong robustness and adaptability.