Abstract : The detection of inshore and offshore ships is an important issue in both military and civilian fields. It helps monitoring fisheries, managing maritime traffics, ensuring safety of coast and sea, etc. In operational contexts, ship detection is traditionally performed by a human observer who identifies all kind of ships from visual analysis on remote sensing images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provides regular, worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as a state-of-the-art solution for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection and recognition, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and propose a deep neu-ral network for the detection, classification and length estimation of ships from SAR Sentinel-1 data. We benefit from synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We then design a multi-task neural-network architecture composed of one joint convolutional network connected to 3 networks dedicated to the different tasks: ship detection, classification and length estimation. Experimental assessment showed our network provides satisfactory results, with accurate classification and length estimation.
