Validation of Targets in Sonar Imagery Using Multispectral Analysis

The detection of underwater objects in sonar imagery is a key enabling technique, for applications ranging from mine hunting and seabed characterization to marine archaeology. Owing to the nonhomogeneity of the sonar imagery, the majority of detection approaches are geared toward the detection of features in the spatial domain to identify anomalies in the seabed's background. Yet, when the seabed is complex and includes rocks and sand ripples, spatial features are hard to discriminate, leading to high false alarm rates. With the aim of validating the detection of man-made objects in complex environments, we utilize the expected spectral diversity of reflections. This way, we can differentiate man-made objects' reflections from the relatively flat frequency response of natural objects' reflections, such as rocks. Our solution merges a set of preregistered sonar images of the same scene that are obtained at a different center frequency. For low-or high-resolution sonar images, we apply the Jain's fairness index or the Kullback-Leibler divergence, respectively, to evaluate the spectral diversity of the reflections of a given region of interest and, thus, detect anomalies across the spectrum domain. We test our algorithm over simulated data and over images collected in three designated sea experiments: a data set that we share with the community. The results show that, compared with benchmark schemes, our solution achieves lower false alarm rates while preserving high detection level.