Side Scan Sonar Object Classification Algorithms

Autonomous underwater vehicles require the capability to understand their environment. This understanding, coupled with the operational goals of the vehicle, determines the subsequent actions of the vehicle. Environmental understanding is realized through the vehicle's sensors and a priori knowledge. This paper focuses on our investigation of automatic interpretation of side scan sonar data for the purpose of detecting and classifying undersea mines. The test data set is a series of side scan sonar images taken from a U. S. Navy acoustic sensor under optimal conditions (flat, sandy bottom). Groundtruth is available for acoustic images with eight unique types of mine targets. The interpretation of the data is per formed in two stages. The first stage, preprocessing and target detection, uses an adaptive thresholding algorithm coupled with an adaptive averaging technique to locate objects of interest in the sonar image. The second stage, classification, performs a binary classification of whether each detected object is, or is not, a mine. The classification is achieved using an attribute-based decision tree. An approach for a third stage, identification of the mark and mod of the classified bottom mines, is also presented. The results to date, as well as plans for the future, are discussed.