Sgformer: A Local and Global Features Coupling Network for Semantic Segmentation of Land Cover-Reference-Cited by-同舟云学术

Sgformer: A Local and Global Features Coupling Network for Semantic Segmentation of Land Cover

Published:2023 Issue: Volume:16 Page:6812-6824
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Weng Liguo¹^ORCID,Pang Kai¹,Xia Min¹^ORCID,Lin Haifeng²^ORCID,Qian Ming³^ORCID,Zhu Changjie⁴

Affiliation:

1. Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing, China

2. College of Information Science and Technology, Nanjing Forestry University, Nanjing, China

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

4. Changzhou Campus, Hohai University, Nanjing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10184025.pdf?arnumber=10184025

Reference64 articles.

1. MCANet: A Multi-Branch Network for Cloud/Snow Segmentation in High-Resolution Remote Sensing Images

2. DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

3. Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image

4. DenseASPP for Semantic Segmentation in Street Scenes

5. Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features

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