Self-Supervised Learning With Multiscale Densely Connected Network for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

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Self-Supervised Learning With Multiscale Densely Connected Network for Hyperspectral Image Classification

Published:2024 Issue: Volume:62 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ye Zhen¹,Cao Zhan¹,Liu Huan²^ORCID,Liu Haipeng¹,Li Wei²^ORCID,Bai Lin¹^ORCID,Li Xiaobo³

Affiliation:

1. School of Electronics and Control Engineering, Chang’an University, Xi’an, China

2. School of Information and Electronics and Beijing Key Laboratory of Fractional Signals and Systems, Beijing Institute of Technology, Beijing, China

3. China Academy of Space Technology (Xi’an), Xi’an, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Beijing Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10587267.pdf?arnumber=10587267

Reference55 articles.

1. Classification of hyperspectral remote sensing images with support vector machines

2. Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

3. Investigation of the random forest framework for classification of hyperspectral data

4. Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

5. Multiarea Target Attention for Hyperspectral Image Classification

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