A Statistically-Based Method for the Detection of Underwater Objects in Sonar Imagery

We offer a new unsupervised statistically-based algorithm for the detection of underwater objects in synthetic aperture sonar (SAS) imagery due to its high-resolution imagery and because its resolution is independent of the range. In contrast to other methods that do not utilize the statistical model of the shadow region, our algorithm combines highlight detection and shadow detection using a weighted likelihood ratio test, while exploiting the expected spatial distribution of potential objects. We detect highlights by a higher-order-statistics representation of the image, followed by a segmentation process to form a region-of-interest (ROI). Then, while taking into account the sonar elevation and scan angle, for each ROI, we use a support vector machine (SVM) over the statistical features of the pixels within the ROI to detect shadow-related pixels and background pixels. Our algorithm has the benefit of being robust as a result of setting its main parameters in situ. Moreover, we do not require knowledge about the target's shape or size, thereby making our algorithm suitable for all sonar detection applications and sonar types. To test detection performance, using our own autonomous underwater vehicle, we collected 270 sonar images, which we also share with the community. Compared to the results of benchmark schemes, our detection algorithm shows a trade-off between the probability of detection and the false alarm rate (FAR), which is close to the Kullback-Leibler (KL) divergence lower bound.