Ship Detection Without Sea-Land Segmentation for Large-Scale High-Resolution Optical Satellite Images

Ship detection is an important and challenging topic in remote sensing applications. In current literatures, sea-land segmentation is generally requested before ship detection. This makes the implementation of the methods highly complicated. Therefore, based on Faster R-CNN, this paper proposes a ship detection method for large-scale images, which does not need sea-land segmentation as preprocessing step and can detect ships directly from complicated background including sea and land. We use large-scale images consisting of GF-1 and GF-2 satellite images to test our network. Experimental results prove that the proposed method plays a role in removing the interference of objects on land.