Neural network based solutions for ship detection in SAR images

Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. Two ship detectors are proposed in this paper. The first one is a MLP-based detector that uses K-distribution parameters to characterize the sea clutter and the brightness of the pixels to detect ships. The second one is the double parameters model, DPM, proposed in the literature. While the DPM-based detector gives rise to some false alarms, leading to the need of a discrimination stage, and has some troubles when the ships are in rough water, the MLP-based detector along with the combination of both sea and ship features obtains better results in terms of detection and false alarm rates.