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

IEEE Access

Jianhua Lu¹  Xiaoming Tao¹  Yiping Duan¹  Chaoyi Han¹  Ren Yang²  Mai Xu²  Xiaowei Qin³ 

[1] Department of Electronic Engineering, Tsinghua University, Beijing, China;School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Information Science and Technology, University of Science and Technology of China, Hefei, China;

关键词: SAR image segmentation;    hierarchical multinomial latent model;    G0 distribution;    Bayesian inference;   

DOI  :  10.1109/ACCESS.2018.2841041

来源: DOAJ

【 摘 要 】

This paper presents a hierarchical multinomial latent model with G⁰ 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 G⁰ 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.

【 授权许可】

Unknown