Hyperspectral and LiDAR Classification via Frequency Domain-Based Network-Reference-Cited by-同舟云学术

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Hyperspectral and LiDAR Classification via Frequency Domain-Based Network

Published:2024 Issue: Volume:62 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Ni Kang¹^ORCID,Wang Duo²^ORCID,Zhao Guofeng³,Zheng Zhizhong¹^ORCID,Wang Peng⁴^ORCID

Affiliation:

1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

2. College of Automation and the College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, China

3. Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology, Nanjing, China

4. Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

China Postdoctoral Science Foundation

Open Foundations of Jiangsu Province Engineering Research Center of Airborne Detecting and Intelligent Perceptive Technology

Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics, Ministry of Education

Jiangsu Geological Bureau Research Project

Nanjing University of Posts and Telecommunications Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10614647.pdf?arnumber=10614647

Reference64 articles.

1. Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning

2. SpectralDiff: A Generative Framework for Hyperspectral Image Classification With Diffusion Models

3. ELS2T: Efficient Lightweight Spectral–Spatial Transformer for Hyperspectral Image Classification

4. Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification

5. Spectral–Spatial Masked Transformer With Supervised and Contrastive Learning for Hyperspectral Image Classification

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