PolSAR Image Classification Based on Statistical Distribution and MRF

Remote Sensing

Chih-Yuan Chu¹  Yang-Lang Chang¹  Jian Yang²  Xiyun Liu³  Junjun Yin³ 

[1] Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;

关键词: polarimetric synthetic aperture radar;    image classification;    statistics;    markov random field;    mixture model;   

DOI  :  10.3390/rs12061027

来源: DOAJ

【 摘 要 】

Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes. In this study, we aim to find a robust and efficient method for polarimetric SAR (PolSAR) image classification. The Markov random field (MRF) has been widely used for capturing the spatial-contextual information of the image. In this paper, we firstly introduce two ways to construct the Wishart mixture model and compare their performances using real PolSAR data. Then, the better mixture model and two other classical statistically distributions are combined with MRF to construct the MRF models. In order to improve the robustness of the models, the constant false alarm rate (CFAR)-based edge penalty term and an adaptive neighborhood system are embedded into the MRF energy functional. Classification is implemented in two schemes, i.e., pixel-based and region-based classifications. Finally, agriculture fields are used as the test scenario to evaluate the robustness and applicability of these algorithms.

【 授权许可】

Unknown