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...
Main Authors: | Hongtai Yao, Xianpei Wang, Le Zhao, Meng Tian, Zini Jian, Li Gong, Bowen Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-12-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/14/1/127 |
Similar Items
-
Enhanced Lightweight End-to-End Semantic Segmentation for High-Resolution Remote Sensing Images
by: He Dong, et al.
Published: (2022-01-01) -
Precise Extraction of Buildings from High-Resolution Remote-Sensing Images Based on Semantic Edges and Segmentation
by: Liegang Xia, et al.
Published: (2021-08-01) -
HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images
by: Zhiyong Xu, et al.
Published: (2020-12-01) -
Multi-Field Context Fusion Network for Semantic Segmentation of High-Spatial-Resolution Remote Sensing Images
by: Xinran Du, et al.
Published: (2022-11-01) -
Semantic Segmentation of High-Resolution Remote Sensing Images Based on Sparse Self-Attention and Feature Alignment
by: Li Sun, et al.
Published: (2023-03-01)