FRF-Net: Land Cover Classification From Large-Scale VHR Optical Remote Sensing Images-Reference-Cited by-同舟云学术

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FRF-Net: Land Cover Classification From Large-Scale VHR Optical Remote Sensing Images

Published:2020-06 Issue:6 Volume:17 Page:1057-1061
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Sang Qianbo^ORCID,Zhuang Yin^ORCID,Dong Shan^ORCID,Wang Guanqun,Chen He

Funder

Chang Jiang Scholars Program

Hundred Leading Talent Project of Beijing Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9097820/08848484.pdf?arnumber=8848484

Cited by 26 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping;Remote Sensing;2024-06-19

2. CaSaFormer: A cross- and self-attention based lightweight network for large-scale building semantic segmentation;International Journal of Applied Earth Observation and Geoinformation;2024-06

3. Optimized convolutional neural network for land cover classification via improved lion algorithm;Transactions in GIS;2024-03-21

4. Knowledge evolution learning: A cost-free weakly supervised semantic segmentation framework for high-resolution land cover classification;ISPRS Journal of Photogrammetry and Remote Sensing;2024-01

5. Remote sensing image semantic segmentation network based on multi-scale feature enhancement fusion;Geocarto International;2024-01

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