From Statistical Detection to Decision Fusion: Detection of Underwater Mines in High Resolution SAS Images

Among all the applications proposed by sonar systems is underwater demining. Indeed, even if the problem is less exposed than the terrestrial equivalent, the presence of underwater mines in waters near the coast and particularly the harbours provoke accidents and victims in fishing and trade activities, even a long time after conflicts. As for terrestrial demining (Milisavljević et al., 2008), detection and classification of various types of underwater mines is currently a crucial strategic task (U.S. Department of the Navy, 2000). Over the past decade, synthetic aperture sonar (SAS) has been increasingly used in seabed imaging, providing high-resolution images (Hayes & Gough, 1999). However, as with any active coherent imaging system, the speckle constructs images with a strong granular aspect that can seriously handicap the interpretation of the data (Abbot & Thurstone, 1979). Many approaches have been proposed in underwater mine detection and classification using sonar images. Most of them use the characteristics of the shadows cast by the objects on the seabed (Mignotte et al., 1997). These methods fail in case of buried objects, since no shadow is cast. That is why this last case has been less studied. In such cases, the echoes (high-intensity reflection of the wave on the objects) are the only hint suggesting the presence of the objects. Their small size, even in SAS imaging, and the similarity of their amplitude with the background make the detection more complex. Starting from a synthetic aperture image, a complete detection and classification process would be composed of three main parts as follows: 1. Pixel level: the decision consists in deciding whether a pixel belongs to an object or to the background. 2. Object level: the decision concerns the segmented object which is “real” or not: are these objects interesting (mines) or simple rocks, wastes? Shape parameters (size,...) and position information can be used to answer this question. 3. Classification of object: the decision concerns the type of object and its identification (type of mine). This chapter deals with the first step of this process. The goal is to evaluate a confidence that a pixel belongs to a sought object or to the seabed. In the following, considering the object O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg