Fast ship detection of synthetic aperture radar images via multi-view features and clustering

This paper proposes a novel ship detection scheme in coastal regions for high-resolution synthetic aperture radar (SAR) imagery based on prior knowledge of the different properties presented by target and clutter. To begin with, image segmentation and land masking are applied to eliminate the areas that are unlikely to contain targets and get the index image which indicates the likely target positions. Ship detection is conducted only on these likely target positions using power ring algorithm (PR), which can avoid unnecessary and exhaustive searches. In the discrimination stage, two new features named number of 8 connected regions and average power of target areas are proposed and used to form a discriminative feature group. Unlike most discriminators, which are based on supervised learning, we use an unsupervised method based on K-means clustering to deal with the situations where there are few or no labeled samples. Experimental results show that the proposed scheme is fast in speed and can detect most of the targets while few false alarms occur.