Semantic-Aware Region Loss for Land-Cover Classification-Reference-Cited by-同舟云学术

Semantic-Aware Region Loss for Land-Cover Classification

Published:2023 Issue: Volume:16 Page:4139-4152
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:

Zheng Xianwei¹^ORCID,Ma Qiyuan¹^ORCID,Huan Linxi¹,Xie Xiao²^ORCID,Xiong Hanjiang¹^ORCID,Gong Jianya³

Affiliation:

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

2. Key Lab for Environmental Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

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

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10097646.pdf?arnumber=10097646

Reference68 articles.

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2. RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks

3. Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation

4. Understanding Convolution for Semantic Segmentation

5. Focal Loss for Dense Object Detection

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