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2024 | OriginalPaper | Buchkapitel

3. Multi-feature Fusion: Graph Neural Network and CNN Combining for Hyperspectral Image Classification

verfasst von : Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang

Erschienen in: Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

Verlag: Springer Nature Singapore

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Abstract

Hyperspectral imagery collected from satellite or airborne comprises hundreds of contiguous bands and contains abundant spectral-spatial information. Due to the advantages of HSI, land-cover categories can be distinguished at the pixel level.

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Vorheriges Kapitel Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification
Nächstes Kapitel Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework for Hyperspectral Image Classification
Literatur
1.
Y. Ding, X. Zhao, Z. Zhang, W. Cai, N. Yang, Graph sample and aggregate-attention network for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
2.
D. Hong, W. He, N. Yokoya, J. Yao, L. Gao, L. Zhang, J. Chanussot, X. Zhu, Interpretable hyperspectral artificial intelligence: when nonconvex modeling meets hyperspectral remote sensing. IEEE Geosci. Remote Sens. Magaz. 9(2), 52–87 (2021)CrossRef
3.
D. Yao, Z. Zhi-li, Z. Xiao-feng, C. Wei, H. Fang, C. Yao-ming, Deep hybrid: multi-graph neural network collaboration for hyperspectral image classification. Def. Technol. (2022)
4.
D. Hong, L. Gao, N. Yokoya, J. Yao, J. Chanussot, Q. Du, B. Zhang, More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Trans. Geosci. Remote Sens. 59(5), 4340–4354 (2020)CrossRef
5.
P. Zhong, Z. Gong, J. Shan, Multiple instance learning for multiple diverse hyperspectral target characterizations. IEEE Trans. Neural Netw. Learn. Syst. 31(1), 246–258 (2019)CrossRef
6.
S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, J.A. Benediktsson, Deep learning for hyperspectral image classification: an overview. IEEE Trans. Geosci. Remote Sens. 57(9), 6690–6709 (2019)CrossRef
7.
D. Hong, N. Yokoya, J. Chanussot, X.X. Zhu, CoSpace: Common subspace learning from hyperspectral-multispectral correspondences. IEEE Trans. Geosci. Remote Sens. 57(7), 4349–4359 (2019)CrossRef
8.
Z.Z.Y. Ding, X. Zhao, D. Hong, W. Li, W. Cai, Y. Zhan, AF2GNN: graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification. Inform. Sci. (2022)
9.
Y. Cai, M. Zeng, Z. Cai, X. Liu, Z. Zhang, Graph regularized residual subspace clustering network for hyperspectral image clustering. Inform. Sci. 578, 85–101 (2021)MathSciNetCrossRef
10.
Y. Ding, X. Zhao, Z. Zhang, W. Cai, N. Yang, Y. Zhan, Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. (2021)
11.
J. Bai, A. Yuan, Z. Xiao, H. Zhou, D. Wang, H. Jiang, L. Jiao, Class incremental learning with few-shots based on linear programming for hyperspectral image classification. IEEE Trans. Cybern. (2020)
12.
J. Peng, L. Li, Y.Y. Tang, Maximum likelihood estimation-based joint sparse representation for the classification of hyperspectral remote sensing images. IEEE Trans. Neural Netw. Learn. Syst. 30(6), 1790–1802 (2018)MathSciNetCrossRef
13.
Y. Cai, Z. Zhang, Z. Cai, X. Liu, X. Jiang, Q. Yan, Graph convolutional subspace clustering: a robust subspace clustering framework for hyperspectral image. IEEE Trans. Geosci. Remote Sens. 59(5), 4191–4202 (2020)CrossRef
14.
J. Bai, S. Huang, Z. Xiao, X. Li, Y. Zhu, A.C. Regan, L. Jiao, Few-shot hyperspectral image classification based on adaptive subspaces and feature transformation. IEEE Trans. Geosci. Remote Sens. 60, 1–17 (2022)
15.
C. Chen, W. Li, H. Su, K. Liu, Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens. 6(6), 5795–5814 (2014)CrossRef
16.
J. Li, J.M. Bioucas-Dias, A. Plaza, Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 48(11), 4085–4098 (2010)
17.
C. Bo, H. Lu, D. Wang, Hyperspectral image classification via JCR and SVM models with decision fusion. IEEE Geosci. Remote Sens. Lett. 13(2), 177–181 (2015)
18.
Y. Cai, X. Liu, Z. Cai, BS-Nets: an end-to-end framework for band selection of hyperspectral image. IEEE Trans. Geosci. Remote Sens. 58(3), 1969–1984 (2019)CrossRef
19.
M. Fauvel, J.A. Benediktsson, J. Chanussot, J.R. Sveinsson, Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 46(11), 3804–3814 (2008)CrossRef
20.
L. Fang, N. He, S. Li, P. Ghamisi, J.A. Benediktsson, Extinction profiles fusion for hyperspectral images classification. IEEE Trans. Geosci. Remote Sens. 56(3), 1803–1815 (2017)CrossRef
21.
S. Jia, L. Shen, Q. Li, Gabor feature-based collaborative representation for hyperspectral imagery classification. IEEE Trans. Geosci. Remote Sens. 53(2), 1118–1129 (2014)
22.
Z. Tang, M. Ling, H. Yao, Z. Qian, X. Zhang, J. Zhang, S. Xu, Robust image hashing via random Gabor filtering and DWT. Comput. Mater. Cont. 55(2), 331–344 (2018)
23.
P. Quesada-Barriuso, F. Argüello, D.B. Heras, Spectral–spatial classification of hyperspectral images using wavelets and extended morphological profiles. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(4), 1177–1185 (2014)CrossRef
24.
J.A. Benediktsson, J.A. Palmason, J.R. Sveinsson, Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 43(3), 480–491 (2005)CrossRef
25.
X. Kang, S. Li, J.A. Benediktsson, Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 52(5), 2666–2677 (2013)CrossRef
26.
X.X. Zhu, D. Tuia, L. Mou, G.-S. Xia, L. Zhang, F. Xu, F. Fraundorfer, Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci. Remote Sens. Magaz. 5(4), 8–36 (2017)CrossRef
27.
L. Mou, P. Ghamisi, X.X. Zhu, Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3639–3655 (2017)CrossRef
28.
Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, Deep learning-based classification of hyperspectral data. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(6), 2094–2107 (2014)CrossRef
29.
K. Makantasis, K. Karantzalos, A. Doulamis, N. Doulamis, Deep supervised learning for hyperspectral data classification through convolutional neural networks, in Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (IEEE, 2015), pp. 4959–4962
30.
S.K. Roy, G. Krishna, S.R. Dubey, B.B. Chaudhuri, HybridSN: exploring 3-D–2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 17(2), 277–281 (2019)CrossRef
31.
W. Hu, Y.Y. Huang, L. Wei, F. Zhang, H.C. Li, Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2015, 1–12 (2015)CrossRef
32.
Y. Li, H. Zhang, Q. Shen, Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1), 67 (2017)CrossRef
33.
J. Yang, Y.-Q. Zhao, J.C.-W. Chan, Learning and transferring deep joint spectral–spatial features for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 55(8), 4729–4742 (2017)CrossRef
34.
C. Chen, J.-J. Zhang, C.-H. Zheng, Q. Yan, L.-N. Xun, Classification of hyperspectral data using a multi-channel convolutional neural network, in International Conference on Intelligent Computing (Springer, 2018), pp. 81–92
35.
J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440
36.
L. Zhu, Y. Chen, P. Ghamisi, J.A. Benediktsson, Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(9), 5046–5063 (2018)CrossRef
37.
H. Hu, M. Yao, F. He, F. Zhang, Graph neural network via edge convolution for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
38.
M.W.T.N. Kipf, Semi-Supervised Classification with Graph Convolutional Networks (2016)
39.
A. Qin, Z. Shang, J. Tian, Y. Wang, T. Zhang, Y.Y. Tang, Spectral–spatial graph convolutional networks for semisupervised hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 16(2), 241–245 (2018)CrossRef
40.
J. Bai, B. Ding, Z. Xiao, L. Jiao, H. Chen, A.C. Regan, Hyperspectral image classification based on deep attention graph convolutional network. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021)
41.
D. Hong, L. Gao, J. Yao, B. Zhang, A. Plaza, J. Chanussot, Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(7), 5966–5978 (2020)CrossRef
42.
S. Wan, C. Gong, P. Zhong, B. Du, L. Zhang, J. Yang, Multiscale dynamic graph convolutional network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(5), 3162–3177 (2019)CrossRef
43.
Y. Ding, X. Zhao, Z. Zhang, W. Cai, N. Yang, Multiscale graph sample and aggregate network with context-aware learning for hyperspectral image classification. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 4561–4572 (2021)CrossRef
44.
A.J. Izenman, Linear discriminant analysis, in Modern Multivariate Statistical Techniques (Springer, 2013), pp. 237–280
45.
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk, SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef
46.
T. Dozat, Incorporating Nesterov Momentum into Adam (2016)
47.
K. Djerriri, A. Safia, R. Adjoudj, M.S. Karoui, Improving hyperspectral image classification by combining spectral and multiband compact texture features, in IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium (IEEE, 2019), pp. 465–468
48.
W. Li, G. Wu, F. Zhang, Q. Du, Hyperspectral image classification using deep pixel-pair features. IEEE Trans. Geosci. Remote Sens. 55(2), 844–853 (2016)CrossRef
49.
C. Zhang, G. Li, S. Du, Multi-scale dense networks for hyperspectral remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 57(11), 9201–9222 (2019)CrossRef
50.
M. Zhang, W. Li, Q. Du, Diverse region-based CNN for hyperspectral image classification. IEEE Trans. Image Process. 27(6), 2623–2634 (2018)MathSciNetCrossRef
Metadaten
Titel
Multi-feature Fusion: Graph Neural Network and CNN Combining for Hyperspectral Image Classification
verfasst von
Yao Ding
Zhili Zhang
Haojie Hu
Fang He
Shuli Cheng
Yijun Zhang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images

Print ISBN: 978-981-9780-08-2

Electronic ISBN: 978-981-9780-09-9

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-8009-9_3

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