The project will develop new and improved algorithms for operational inference of information about the presence and properties of sea vessels based on deep learning in SAR images. Ship detection is the main focus of the project, but extraction of higher-level information, such as ship type, size and heading, is a natural next task. The work will both target existing satellites and prepare for upcoming missions with innovative SAR modes. A subtask is to find strategies for efficient adaptation of developed algorithms to new sensors and SAR modes. This can be done by transfer learning, which includes judicious reuse of certain parts of the deep architectures, while modifying and retraining other parts. Deep learning methods can efficiently exploit contextual information to identify the characteristics of vessels that extend over multiple pixels and reject the typical spatial patterns of range and azimuth ambiguities. Another motivating property is their ability to learn features implicitly from training data, thus avoiding explicit feature extraction and encoding of prior knowledge. Operational ship detection introduces challenges that can be efficiently dealt with by deep learning from example data, including rejection of ambiguities and target look-alikes. The literature holds some examples of successful application of deep learning to ship detection, but much work is still needed to transfer research into an operational systems and obtain the performance required in an environment governed by customer specifications and near real-time constraints, concerning processing speed, detection performance, reliability and robustness. The validation process will utilize the operational processing environment at KSAT with access to large amounts of training data. The project will profit on unique access to multimission SAR data, ground truth data from the Automatic Identification System network, and knowledge about user requirements.
Project leader: Tony Bauna