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Open AccessArticle

PolSAR Image Classification Based on Statistical Distribution and MRF

1
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
3
Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1027; https://doi.org/10.3390/rs12061027
Received: 21 January 2020 / Revised: 7 March 2020 / Accepted: 19 March 2020 / Published: 23 March 2020
(This article belongs to the Section Remote Sensing Image Processing)
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. View Full-Text
Keywords: polarimetric synthetic aperture radar; image classification; statistics; Markov random field; mixture model polarimetric synthetic aperture radar; image classification; statistics; Markov random field; mixture model
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MDPI and ACS Style

Yin, J.; Liu, X.; Yang, J.; Chu, C.-Y.; Chang, Y.-L. PolSAR Image Classification Based on Statistical Distribution and MRF. Remote Sens. 2020, 12, 1027.

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