Purified Contrastive Learning With Global and Local Representation for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Purified Contrastive Learning With Global and Local Representation for Hyperspectral Image Classification

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhao Lin¹^ORCID,Li Jia¹^ORCID,Luo Wenqiang¹^ORCID,Ouyang Er²^ORCID,Wu Jianhui¹^ORCID,Zhang Guoyun¹^ORCID,Li Wujin¹^ORCID

Affiliation:

1. Hunan Engineering Technology Research Center for 3D Reconstruction and Intelligent Application, Hunan Institute of Science and Technology, Yueyang, China

2. Shenzhen University, Shenzhen, China

Funder

Natural Science Foundation of Hainan Province

Scientific Research Projection of the Education Department of Hunan Province

Graduate Research and Innovation Project of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10547362.pdf?arnumber=10547362

Reference56 articles.

1. HyperLoopNet: Hyperspectral image classification using multiscale self-looping convolutional networks

2. CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection

3. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

4. Multiple Feature Learning for Hyperspectral Image Classification

5. Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification

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