SHIP DETECTION FROM OPTICAL REMOTE SENSING IMAGERY BASED ON SCENE CLASSIFICATION AND SALIENCY-TUNED RETINANET

Due to the variation of ocean scene, such as thin cloud occlusion, thick cloud and sea surface clutter interferences, ship detection from optical remote sensing images often suffer from high false alarm rate. As a consequence, a ship detection method based on background classification and saliency-tuned RetinaNet is proposed to address this problem. First, the ocean scene is divided into four categories: thin cloud and fog occlusion scene, thick cloud interference scene, shore scene and calm sea scene by a designed scene classification module (SCM). Second, a multi-scale saliency feature fusion module (MSFM) is designed to provide more discriminative features for ship detection. In particular, by associating saliency maps and feature maps, our proposed MSFM can effectively suppress backgrounds noise. Finally, the MSFM are integrated into the rotation single-stage detector to effectively identify arbitrary-oriented ships from complex ocean scene. Extensive experiments on optical remote sensing dataset demonstrated that the proposed method can obtain better detection performance than the state-of-the-art methods and achieved a comparable detection speed.