Sonar image segmentation using a multi-spatial information constraint fuzzy C-means clustering algorithm based on KL divergence

Sonar image segmentation is an important task in the field of underwater detection, and the realization of accurate segmentation of targets and shadows is the key to subsequent image processing. However, due to the influence of various marine environments, the formation of sonar images is often accompanied by high scattering noise and intensity inhomogeneity. In order to solve the difficulties brought by the above reasons to sonar image segmentation, this paper proposes a multi-spatial information constrained fuzzy c-means clustering algorithm (MSCFCM). Firstly, we incorporate local spatial information into the MSCFCM through morphological reconstruction (MR), and construct the distance metric between the current pixel and its neighboring pixels by combining the mean value information of the image, removing a large amount of noise in the background; secondly, we use the difference in the normalized variance of the processed image and the original image as a weight to constrain the influence of the distance terms, and embed it adaptively into the fuzzy clustering algorithm; finally, the member information of the neighboring pixels is used as the prior information of the current pixel by using the Kullback–Leibler (KL) divergence, and the division of membership degree in each iteration can be optimized to further improve the segmentation performance. We test our method on sonar images and medical images, and the experimental results demonstrate that the algorithm exhibits strong segmentation performance and noise-immunity.