Semantic Segmentation of Remote Sensing Imagery Using an Object-Based Markov Random Field Model With Auxiliary Label Fields

2017 ◽  
Vol 55 (5) ◽  
pp. 3015-3028 ◽  
Author(s):  
Chen Zheng ◽  
Yun Zhang ◽  
Leiguang Wang
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 ◽  
Object Based

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 SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 75-81
Author(s):  
L. He ◽  
Z. Wu ◽  
Y. Zhang ◽  
Z. Hu
Keyword(s):  
Remote Sensing ◽  
Random Field ◽  
Markov Random Field ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
Hierarchical Segmentation ◽  
Art Object ◽  
Markov Random ◽  
Object Based

Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.

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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