Sonar image segmentation using an unsupervised hierarchical MRF model

M. Mignotte, C. Collet, P. Perez, P. Bouthemy
2000 IEEE Transactions on Image Processing  
This paper is concerned with hierarchical Markov random field (MRF) models and their application to sonar image segmentation. We present an original hierarchical segmentation procedure devoted to images given 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 sea-bed) and sea-bottom reverberation. The proposed unsupervised scheme takes into account the variety of the laws
more » ... the distribution mixture of a sonar image, and it estimates both the parameters of noise distributions and the parameters of the Markovian prior. For the estimation step, we use an iterative technique which combines a maximum likelihood approach (for noise model parameters) with a least-squares method (for MRF-based prior). In order to model more precisely the local and global characteristics of image content at different scales, we introduce a hierarchical model involving a pyramidal label field. It combines coarse-to-fine causal interactions with a spatial neighborhood structure. This new method of segmentation, called scale causal multigrid (SCM) algorithm, has been successfully applied to real sonar images and seems to be well suited to the segmentation of very noisy images. The experiments reported in this paper demonstrate that the discussed method performs better than other hierarchical schemes for sonar image segmentation. Index Terms-Hierarchical MRF, parameter estimation, sonar imagery, unsupervised segmentation. 1 Moreover, the classification method proposed in [5] is applied to images provided by a low frequency sonar, where the pixel resolution is around 400 2400 m. We suspect that the proposed model is not well-suited to the high resolution sonar images we wish to segment for object classification purposes.
doi:10.1109/83.847834 pmid:18262959 fatcat:kz3ma6lv2rfedc25qduu2zcnpi