Lightweight omni-dimensional dynamic convolution with spatial-spectral self-attention network for hyperspectral image classification-Reference-Cited by-同舟云学术

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Lightweight omni-dimensional dynamic convolution with spatial-spectral self-attention network for hyperspectral image classification

Published:2024-08-30 Issue:18 Volume:45 Page:6572-6600
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Liu Yi¹²,Peng Xufeng³,Zhang Yanjun¹

Affiliation:

1. School of Instrument and Electronics, North University of China, Taiyuan, People’s Republic of China

2. Department of Automation, Taiyuan Institute of Technology, Taiyuan, People’s Republic of China

3. The Department of Remote Sensing Technology, Beijing Institute of Astronautical Systems Engineering, Beijing, People’s Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2024.2391583

Reference63 articles.

1. Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network

2. Dual-stream spectral-spatial convolutional neural network for hyperspectral image classification and optimal band selection

3. Application of airborne and spaceborne hyperspectral imaging techniques for atmospheric research: past, present, and future

4. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

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