CGGLNet: Semantic Segmentation Network for Remote Sensing Images Based on Category-Guided Global–Local Feature Interaction
Author:
Affiliation:
1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China
2. State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University, Wuhan, China
Funder
Innovative Talent Program of Jiangsu
National Science and Technology Major Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/36/10354519/10475326.pdf?arnumber=10475326
Reference53 articles.
1. Remote sensing image segmentation advances: A meta-analysis
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3. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
4. From local context-aware to non-local: A road extraction network via guidance of multi-spectral image
5. Dual-Field-of-View Context Aggregation and Boundary Perception for Airport Runway Extraction
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