SELM: Self-Motivated Ensemble Learning Model for Cross-Domain Few-Shot Classification in Hyperspectral Images-Reference-Cited by-同舟云学术

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SELM: Self-Motivated Ensemble Learning Model for Cross-Domain Few-Shot Classification in Hyperspectral Images

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Zhao Shiyuan¹,Bai Yu²^ORCID,Shao Shuai³^ORCID,Liu Weifeng²^ORCID,Ge Xinmin⁴,Li Ye⁵,Liu Baodi²^ORCID

Affiliation:

1. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, China

2. College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

3. Zhejiang Lab, Hangzhou, China

4. College of Earth Science and Technology, China University of Petroleum (East China), Qingdao, China

5. Department of Economics and Management, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

Funder

National Natural Science Foundation of China

Shandong Natural Science Foundation

Outstanding Youth Science Foundation Project of Shandong Province

Qingdao Natural Science Foundation

China University of Petroleum

Major Scientific and Technological Projects of China National Petroleum Corporation

Creative Research Team of Young Scholars at Universities in Shandong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10443952.pdf?arnumber=10443952

Reference17 articles.

1. HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification

2. Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification

3. Deformable Convolutional Neural Networks for Hyperspectral Image Classification

4. Artificial Intelligence for Remote Sensing Data Analysis: A review of challenges and opportunities

5. Nonlocal Low-Rank Tensor Completion for Visual Data

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