Multiscale segmentation of textured sonar images using cooccurrence statistics

A new method for the segmentation of textured backscattering strength (BS) sonar images is presented. The method is based on the analysis of joint wavelet statistics by using the whole information brought by cooccurrence distributions. After the wavelet transform of the image, on the most informative frequency bands of the wavelet transform, we discriminate between textures by directly measuring the similarity between co-occurrence statistics. Then, we fuse the different segmentations according to the weighted voting rule. Results on real sonar images and textures from the Brodatz album illustrate the effectiveness of the scheme. Finally, performances and results are discussed.