An unsupervised sonar images segmentation approach

Abstract: In this work an unsupervised Sonar (Sound navigation and ranging) images segmentation is proposed. Due to the textural nature of the Sonar images, a band-pass filtering that takes into account the local spatial frequency of these images is proposed. Sonar image is passed through a bank of Gabor filters and the filtered images that possess a significant component of the original image are selected. To calculate the radial frequencies, a new approach is proposed. The selected filtered images are then subjected to a non-linear transformation. An energy measure is defined on the transformed images in order to compute texture features. The texture energy features are used as input to a clustering algorithm. The segmentation scheme has been successfully tested on real high-resolution Sonar images, yielding very promising results.