Multi-frequency backscatter variations for classification of mine-like targets from low-resolution sonar data

The purpose of this research is to classify bottom targets as mine like using low-resolution sonar data. The sonar is mounted on an autonomous, unmanned seafloor crawler, which operates in the littoral regions. Due to the sonar´s relatively large beamwidths and proximity to the seafloor, low-resolution imagery is produced in which objects possess Little discernable shape and no acoustic shadow (i.e., image-based classification is infeasible). Therefore, this research forms multiple images with the sonar operating at different, narrowband frequencies. A feature set is then constructed based on backscatter variations across these various narrowband frequency ranges. In addition to representing target response versus frequency, this feature set also possesses the desirable characteristic of range independence. Afterwards, an unsupervised self-organizing map artificial neural network is trained to project and cluster the features into a two-dimensional feature space. In this space, the decision surfaces are formed and the cluster identities are labeled. Experimental results from a variety of target types illustrate the favorable separability of the clusters and the discriminating power of the frequency-based feature set.