Multi-scale feature learning via residual dynamic graph convolutional network for hyperspectral image classification-Reference-Cited by-同舟云学术

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Multi-scale feature learning via residual dynamic graph convolutional network for hyperspectral image classification

Published:2024-01-28 Issue:3 Volume:45 Page:863-888
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Chen Rong¹,Vivone Gemine²,Li Guanghui¹,Dai Chenglong¹,Chanussot Jocelyn³^ORCID

Affiliation:

1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

2. National Research Council, Institute of Methodologies for Environmental Analysis, Potenza, Italy

3. GIPSA-Lab, University of Grenoble Alpes, Grenoble, France

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2305179

Reference55 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. Deep Learning With Attribute Profiles for Hyperspectral Image Classification

3. Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis

4. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

5. Cascaded dual-scale crossover network for hyperspectral image classification

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LIVEnet: Linguistic-Interact-With-Visual Engager Domain Generalization for Cross-Scene Hyperspectral Imagery Classification;IEEE Geoscience and Remote Sensing Letters;2024

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