Image Processing Techniques For the Detection and Classification of Man Made Objects in Side-Scan Sonar Images

Abstract

This is a review chapter that surveys past work in, and the recent status of image processing and other related techniques involved in the detection and classification of man made objects in side scan sonar images. Side scan sonar is a readily, available and cheap device which has found increasing applications, specially for military purposes such as Computer Aided Detection (CAD) and Classification (CAC) of mines. Therefore the main focus of the chapter is on this topic. The list of references is sufficiently complete to include most past and recent publications in the open refereed literature. Although side scan sonar displays many features similar to an optical sensor from a purely image processing point of view, the basics of the physics and formation of the images are crucial for understanding the difficulties found when detecting and classifying mine like objects (MLO’s) in side-scan sonar images. Therefore in the first part of the chapter a brief review of the principles of the side-scan sonar, image formation process and characteristics of the images are explained. Different types of sonar images as well as diagrams showing the the process of generating an image from a single diagram ping will be provided. The classification and detection of MLO’s is traditionally carried out by a skilled human operator. This analysis is difficult due to the large variability in the appearance of the side-scan images as well as the high levels of noise usually present in the images. With the recent advances of Autonomous Underwater Vehicle (AUV) automatic techniques, CAD/CAC of mines, are now required to replace a human operator. In the literature the computer aided detection/classification (CAD/CAC) problem is not well defined as detection involves an element of classification (mine/not mine), therefore these terms must be defined. For the purpose of this work, we will consider detection as the process of identifying a mine and classification will be a further step where the aim is to determine the shape of such a mine. Therefore the second part of the chapter is divided into two main sections: 1) detection 2) classification of MLO’s. In the last part of this chapter a review will be done on the current state of fusion of multiple algorithms aiming to overcome the limitations and weaknesses of every single CAC/CAD algorithm reviewed in the previous section. Image Processing Techniques for the Detection and Classification of Man Made Objects in Side-Scan Sonar Images 7