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Feature Fusion: Graph Attention Network and CNN Combing for Hyperspectral Image Classification

Authors:

Cao GuoAuthors Info & Claims

ICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision

Pages 171 - 178

https://doi.org/10.1145/3561613.3561640

Published: 09 November 2022 Publication History

Abstract

Graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image classification. However, most of the available GCN-based HSI classification methods treat superpixels as graph nodes, ignoring pixel-level spectral spatial features. In this paper, we propose a novel Feature Fusion Network (FFGCN), which is composed of two different convolutional networks, namely Graph Attention Network (GAT) and Convolutional Neural Network (CNN). Among them, superpixel-based GAT can deal with the problem of labeled deficiency and extract spatial features from HSI. Attention-based multi-scale CNN can extract multi-scale pixel local features for HSI classification. Finally, the features of the two neural network models are fused and used for classification. Rigorous experiments on two real HSI datasets show that FFGCN achieves better experimental results and is competitive with other state-of-the-art methods.

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Cited By

Mu CLiu YYan XAli ALiu Y(2024)Few-Shot Open-Set Hyperspectral Image Classification With Adaptive Threshold Using Self-Supervised Multitask LearningIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.344161762(1-18)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3441617
Jamshidi MKalhor AVahabie A(2024)Active Data Fusion in Deep Neural Networks via Separation Index2024 32nd International Conference on Electrical Engineering (ICEE)10.1109/ICEE63041.2024.10668072(1-7)Online publication date: 14-May-2024
https://doi.org/10.1109/ICEE63041.2024.10668072
Geetha TRao CChellaswamy CUmamaheswari K(2024)Enhancing remote target classification in hyperspectral imaging using graph attention neural networkJournal of Earth System Science10.1007/s12040-024-02294-3133:2Online publication date: 11-May-2024
https://doi.org/10.1007/s12040-024-02294-3

Index Terms

Feature Fusion: Graph Attention Network and CNN Combing for Hyperspectral Image Classification
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Semi-supervised learning

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ICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision

August 2022

241 pages

ISBN:9781450397315

DOI:10.1145/3561613

Copyright © 2022 ACM.

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Publication History

Published: 09 November 2022

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ICCCV 2022

ICCCV 2022: 2022 The 5th International Conference on Control and Computer Vision

August 19 - 21, 2022

Xiamen, China

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Cited By

Mu CLiu YYan XAli ALiu Y(2024)Few-Shot Open-Set Hyperspectral Image Classification With Adaptive Threshold Using Self-Supervised Multitask LearningIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.344161762(1-18)Online publication date: 2024
https://doi.org/10.1109/TGRS.2024.3441617
Jamshidi MKalhor AVahabie A(2024)Active Data Fusion in Deep Neural Networks via Separation Index2024 32nd International Conference on Electrical Engineering (ICEE)10.1109/ICEE63041.2024.10668072(1-7)Online publication date: 14-May-2024
https://doi.org/10.1109/ICEE63041.2024.10668072
Geetha TRao CChellaswamy CUmamaheswari K(2024)Enhancing remote target classification in hyperspectral imaging using graph attention neural networkJournal of Earth System Science10.1007/s12040-024-02294-3133:2Online publication date: 11-May-2024
https://doi.org/10.1007/s12040-024-02294-3

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