Wetland information extraction of remote sensing imagery based on Markov random field theory

2010 ◽  
Author(s):  
Dengrong Zhang ◽  
Yang Wu
Keyword(s):  
Field Theory ◽  
Remote Sensing ◽  
Information Extraction ◽  
Random Field ◽  
Markov Random Field ◽  
Random Field Theory ◽  
Remote Sensing Imagery ◽  
Markov Random

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.

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