An effective false-alarm removal method based on OC-SVM for SAR ship detection

Automatic ship detection from SAR remote sensing imagery is very important, with a wide array of applications in areas such as national marine safety, vessel traffic services, and naval warfare. However, SAR remote sensing images with the high inhomogeneity of sea clutter and complex scene result in a large number of false alarms. This paper focuses on the problem of false alarm removal and coverts it as a classification problem with respect to ship targets and false alarms. However, in real application: (1) Compared to false alarms the number of ships is much smaller, and (2) false alarms with diverse categories and no uniform characteristics are difficult to be distinguished from ships. To solve the problems above, we present a new method based on one-class SVM (OCSVM) combined with grid optimization that only use the data of ships to train classifier in the absence of false alarms. First, 2P-CFAR is adopted to extract ship candidates and 14 features mainly focusing on ship targets are selected. Second, grid optimization is exploited to obtain parameters for OC-SVM training, where it is a compromise between the probability of false alarm and detection rate. Experimental results of OC-SVM on a large SAR image set demonstrate that in comparison with other state-of-the-art methods like SVM, BP neural network, our approach can achieve higher detection accuracy.