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This paper presents a hierarchical multinomial latent model with G<sup>0</sup> distribution for synthetic aperture radar (SAR) image semantic segmentation. The model considers the scattering statistics and multiscale characteristics of the SAR images. Specifically, a 2-D discrete wavelet transform is used to construct a quad-tree structure, and a hierarchical multinomial latent model is used for semantic segmentation. The hierarchical model can capture local and global characteristics of the SAR images. Additionally, statistical distributions of SAR images are discussed. The flexible G<sup>0</sup> distribution is substituted into the hierarchical multinomial latent model for the segmentation of various types of land covers. Moreover, the developed Bayesian inference on the quad-tree structure is incorporated into our approach. The semantic segmentation map is obtained from an initialization, bottom-up, and top-down probability computations. In this way, the underlying spatial structure information is effectively propagated. Our experiments compare the proposed approach, other multinomial latent model-based methods, and Markov random field-based methods. The experiments are conducted from a series of synthetic and real SAR images, where the segmentation results demonstrate that our approach is robust to the noise in most cases, obtains the best result among the compared methods and improves the state-of-the-art segmentation performance.

Hierarchical Multinomial Latent Model With G0 Distribution for Synthetic Aperture Radar Image Semantic Segmentation

Hierarchical Multinomial Latent Model With G⁰ Distribution for Synthetic Aperture Radar Image Semantic Segmentation