A Hybrid Markov Random Field Model With Multi-Granularity Information for Semantic Segmentation of Remote Sensing Imagery

2019 ◽  
Vol 12 (8) ◽  
pp. 2728-2740 ◽  
Author(s):  
Chen Zheng ◽  
Leiguang Wang ◽  
Xiaohui Chen
Keyword(s):  
Remote Sensing ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Semantic Segmentation ◽  
Random Field Model ◽  
Remote Sensing Imagery ◽  
Markov Random Field Model ◽  
Markov Random

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

2019 ◽  
Vol 11 (23) ◽  
pp. 2878
Author(s):  
Chen Zheng ◽  
Xinxin Pan ◽  
Xiaohui Chen ◽  
Xiaohui Yang ◽  
Xin Xin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Semantic Segmentation ◽  
Processing Scheme ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Object Based

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.

scholarly journals INDOOR SEMANTIC SEGMENTATION FROM RGB-D IMAGES BY INTEGRATING FULLY CONVOLUTIONAL NETWORK WITH HIGHER-ORDER MARKOV RANDOM FIELD

2018 ◽  
Vol XLII-4 ◽  
pp. 717-724
Author(s):  
J. Yang ◽  
Z. Kang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Field Model ◽  
Semantic Segmentation ◽  
Spatial Location ◽  
Higher Order ◽  
Convolutional Network ◽  
Random Field Model ◽  
Markov Random Field Model ◽  
Markov Random

<p><strong>Abstract.</strong> Indoor scenes have the characteristics of abundant semantic categories, illumination changes, occlusions and overlaps among objects, which poses great challenges for indoor semantic segmentation. Therefore, we in this paper develop a method based on higher-order Markov random field model for indoor semantic segmentation from RGB-D images. Instead of directly using RGB-D images, we first train and perform RefineNet model only using RGB information for generating the high-level semantic information. Then, the spatial location relationship from depth channel and the spectral information from color channels are integrated as a prior for a marker-controlled watershed algorithm to obtain the robust and accurate visual homogenous regions. Finally, higher-order Markov random field model encodes the short-range context among the adjacent pixels and the long-range context within each visual homogenous region for refining the semantic segmentations. To evaluate the effectiveness and robustness of the proposed method, experiments were conducted on the public SUN RGB-D dataset. Experimental results indicate that compared with using RGB information alone, the proposed method remarkably improves the semantic segmentation results, especially at object boundaries.</p>

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