Joint Classification of Hyperspectral Image and LiDAR Data Based on Spectral Prompt Tuning-Reference-Cited by-同舟云学术

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Joint Classification of Hyperspectral Image and LiDAR Data Based on Spectral Prompt Tuning

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Kong Yi¹^ORCID,Cheng Yuhu¹^ORCID,Chen Yang²^ORCID,Wang Xuesong¹^ORCID

Affiliation:

1. Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, and the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

2. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10568205.pdf?arnumber=10568205

Reference49 articles.

1. Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval

2. MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images

3. Diversity-Connected Graph Convolutional Network for Hyperspectral Image Classification

4. Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

5. Spatial-Spectral Unified Adaptive Probability Graph Convolutional Networks for Hyperspectral Image Classification

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