Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery

Abstract This work deals with unsupervised sonar image segmentation. We present a new estimation and segmentation procedure on images provided by a high-resolution sonar. The sonar image is segmented into two kinds of regions: shadow (corresponding to a lack of acoustic reverberation behind each object lying on the seabed) and reverberation (due to the reflection of acoustic wave on the seabed and on the objects). The unsupervised contextual method we propose is defined as a two-step process. Firstly, the iterative conditional estimation is used for the estimation step in order to estimate the noise model parameters and to accurately obtain the proportion of each class in the maximum likelihood sense. Then, the learning of a Kohonen self-organizing map (SOM) is performed directly on the input image to approximate the discriminating functions, i.e. the contextual distribution function of the grey levels. Secondly, the previously estimated proportion, the contextual information and the Kohonen SOM, after learning, are then used in the segmentation step in order to classify each pixel on the input image. This technique has been successfully applied to real sonar images, and is compatible with an automatic processing of massive amounts of data.