An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image

The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to miscla...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 1; p. 127
Main Authors Yao, Hongtai, Wang, Xianpei, Zhao, Le, Tian, Meng, Jian, Zini, Gong, Li, Li, Bowen
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2022
Subjects
Accuracy
Algorithms
correlation assumption
Fields (mathematics)
high spatial resolution remote sensing image
Image processing
Image reconstruction
Image segmentation
Methods
Neighborhoods
object-based Markov random field
Remote sensing
Semantic segmentation
Semantics
Spatial discrimination
Spatial resolution
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Summary:The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14010127