MASSFormer: Memory-Augmented Spectral-Spatial Transformer for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

MASSFormer: Memory-Augmented Spectral-Spatial Transformer 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:

Sun Le¹^ORCID,Zhang Hang²^ORCID,Zheng Yuhui³^ORCID,Wu Zebin⁴^ORCID,Ye Zhonglin³,Zhao Haixing⁵

Affiliation:

1. School of Computer Science and Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

2. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

3. College of Computer, Qinghai Normal University, Xining, China

4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

5. State Key Laboratory of Tibetan Intelligent Information Processing and Application, Qinghai Normal University, Xining, China

Funder

National Natural Science Foundation of China

Construction Project for Innovation Platform of Qinghai Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10506482.pdf?arnumber=10506482

Reference62 articles.

1. CRNet: Channel-Enhanced Remodeling-Based Network for Salient Object Detection in Optical Remote Sensing Images

2. Hyperspectral Target Detection via Spectral Aggregation and Separation Network With Target Band Random Mask

3. Low-Rank and Sparse Decomposition With Mixture of Gaussian for Hyperspectral Anomaly Detection

4. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance

5. Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

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