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Remote Sensing
Volume 11
Issue 23
10.3390/rs11232878
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Article

An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery

by
Chen Zheng
1,2,3,
Xinxin Pan
1,
Xiaohui Chen
4,*,
Xiaohui Yang
1,3,
Xin Xin
1 and
Limin Su
1
1
School of Mathematics and Statistics, Henan University, Kaifeng 475000, China
2
Department of Geodesy and Geomatics Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
3
Data Analysis Technology Lab, Institute of Applied Mathematics, Henan University, Kaifeng 475000, China
4
Technology Search Station, Henan University, Kaifeng 475000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2878; https://doi.org/10.3390/rs11232878
Submission received: 19 September 2019 / Revised: 15 November 2019 / Accepted: 30 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Image Processing and Spatial Neighbourhoods for Remote Sensing Data Analysis)
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Abstract

The Markov random field model (MRF) has attracted a lot of attention in the field of remote sensing semantic segmentation. But, most MRF-based methods fail to capture the various interactions between different land classes by using the isotropic potential function. In order to solve such a problem, this paper proposed a new generalized probability inference with an anisotropic penalty for the object-based MRF model (OMRF-AP) that can distinguish the differences in the interactions between any two land classes. Specifically, an anisotropic penalty matrix was first developed to describe the relationships between different classes. Then, an expected value of the penalty information (EVPI) was developed in this inference criterion to integrate the anisotropic class-interaction information and the posteriori distribution information of the OMRF model. Finally, by iteratively updating the EVPI terms of different classes, segmentation results could be achieved when the iteration converged. Experiments of texture images and different remote sensing images demonstrated that our method could show a better performance than other state-of-the-art MRF-based methods, and a post-processing scheme of the OMRF-AP model was also discussed in the experiments.
Keywords: semantic segmentation; object-based Markov random field; anisotropic penalty matrix semantic segmentation; object-based Markov random field; anisotropic penalty matrix
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MDPI and ACS Style

Zheng, C.; Pan, X.; Chen, X.; Yang, X.; Xin, X.; Su, L. An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery. Remote Sens. 2019, 11, 2878. https://doi.org/10.3390/rs11232878

AMA Style

Zheng C, Pan X, Chen X, Yang X, Xin X, Su L. An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery. Remote Sensing. 2019; 11(23):2878. https://doi.org/10.3390/rs11232878

Chicago/Turabian Style

Zheng, Chen, Xinxin Pan, Xiaohui Chen, Xiaohui Yang, Xin Xin, and Limin Su. 2019. "An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery" Remote Sensing 11, no. 23: 2878. https://doi.org/10.3390/rs11232878

APA Style

Zheng, C., Pan, X., Chen, X., Yang, X., Xin, X., & Su, L. (2019). An Object-Based Markov Random Field Model with Anisotropic Penalty for Semantic Segmentation of High Spatial Resolution Remote Sensing Imagery. Remote Sensing, 11(23), 2878. https://doi.org/10.3390/rs11232878

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