nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

4. Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework 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

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Hyperspectral images (HSIs) collect rich spatial-spectral information in hundreds of spectral bands, which are captured by hyperspectral remote sensors, (Rasti et al., IEEE Geosci Remote Sens Mag 8(4):60–88, 2020; Ghamisi et al., IEEE Geosci Remote Sens Mag 5(4):37–78, 2017; Peng et al., IEEE Trans Geosci Remote Sens 57(2):1183–1194, 2018; Lu et al., IEEE Trans Geosci Remote Sens 56(4):2183–2195, 2018), which are more effective in distinguishing different land-covers compared with other multispectral (Zhao et al., IEEE Trans Neural Netw Learn Syst 30(11):3212–3232, 2019) or RGB (red, green, and blue) image (Hong et al., IEEE Geosci Remote Sens Mag 11:4051, 2021). Therefore, HSIs are widely employed in various applications, ranging from military reconnaissance, marine monitoring to disaster prevention and control (Hong et al., IEEE Geosci Remote Sens 9:16820, 2020; Ding et al., IEEE Geosci Remote Sens Lett 19:1–5, 2021). HSI classification (category each pixel into certain label) is a crucial technique for these applications. However, the complex noise effects and spectral variability (Hong et al., IEEE Trans Image Process 28(4):1923–1938, 2019), labeled training samples deficiency (Wan et al., IEEE Trans Geosci Remote Sens 59(1):597–612, 2021), and high spectral mixing between materials (Zhong et al., IEEE Trans Neural Netw Learn Syst 14:1–13, 2019) bring difficulties in extracting discriminative information from HSI for classification.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Multi-feature Fusion: Graph Neural Network and CNN Combining for Hyperspectral Image Classification

Nächstes Kapitel Graph Neural Network via Edge Convolution for Hyperspectral Image Classification

B. Rasti et al., Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox. IEEE Geosci. Remote Sens. Mag. 8(4), 60–88 (2020). https://doi.org/10.1109/-MGRS.2020.2979764CrossRef

P. Ghamisi, N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, B. Rasti, A. Plaza, Advances in hyperspectral image and signal processing: a comprehensive overview of the state of the art. IEEE Geosci. Remote Sens. Mag. 5(4), 37–78 (2017)CrossRef

J. Peng, W. Sun, Q. Du, Self-paced joint sparse representation for the classification of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 57(2), 1183–1194 (2018)CrossRef

X. Lu, B. Wang, X. Zheng, X. Li, Exploring models and data for remote sensing image caption generation. IEEE Trans. Geosci. Remote Sens. 56(4), 2183–2195 (2018)CrossRef

Z. Zhao, P. Zheng, S. Xu, X. Wu, Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)CrossRef

D. Hong, W. He, N. Yokoya, J. Yao, L. Gao, L. Zhang, J. Chanussot, X. Zhu, Interpretable hyperspectral AI: when non-convex modeling meets hyperspectral remote sensing. IEEE Geosci. Remote Sens. Mag. 11, 4051 (2021). https://doi.org/10.1109/MGRS.2021.3064051CrossRef

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 Geosci. Remote Sens. 9, 16820 (2020). https://doi.org/10.1109/TGRS.2020.3016820CrossRef

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 (2021). https://doi.org/10.1109/LGRS.2021.306-2944CrossRef

D. Hong, N. Yokoya, J. Chanussot, X. Zhu, An augmented linear mixing model to address spectral variability for hyperspectral unmixing. IEEE Trans. Image Process. 28(4), 1923–1938 (2019)MathSciNetCrossRef

10.

S. Wan, C. Gong, P. Zhong, S. Pan, G. Li, J. Yang, Hyperspectral image classification with context-aware dynamic graph convolutional network. IEEE Trans. Geosci. Remote Sens. 59(1), 597–612 (2021)CrossRef

11.

P. Zhong, Z. Gong, J. Shan, Multiple instance learning for multiple diverse hyperspectral target characterizations. IEEE Trans. Neural Netw. Learn. Syst. 14, 1–13 (2019)

12.

J. Feng et al., Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification. Remote Sens. 12(7), 1149 (2020)CrossRef

13.

M. Pesaresi, A. Gerhardinger, F. Kayitakire, A robust built-up area presence index by anisotropic rotation-invariant textural measure. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 1(3), 180–192 (2008)CrossRef

14.

K. Djerriri, A. Safia, R. Adjoudj, M.S. Karoui, Improving hyperspec-tral image classification by combining spectral and multiband compact texture features, in Proceedings of the IEEE International Geosciences Remote Sensing Symposium (IGARSS) (2019), pp. 465–468

15.

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 (2017)CrossRef

16.

C. Liu, J. Li, L. He, A. Plaza, S. Li, B. Li, Naive gabor networks for hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 376–390 (2020)MathSciNetCrossRef

17.

Y. Cai, X. Liu, Z. Cai, BS-Nets: an end-to-end framework for band selection of hyperspectral image. IEEE Geosci. Remote Sens. 58, 1969–1984 (2020)CrossRef

18.

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

19.

Y. Chen, X. Zhao, X. Jia, Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 8(6), 2381–2392 (2015)CrossRef

20.

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

21.

M. Zhang, W. Li, Q. Du, Diverse region-based CNN for hyperspectral image classification. IEEE Trans. Image Process. 27(6), 2623–2634 (2018)MathSciNetCrossRef

22.

T. Li, J. Zhang, Y. Zhang, Classification of hyperspectral image based on deep belief networks, in Proceedings of the IEEE ICIP (2014), pp. 5132–5136

23.

R. Hang, Q. Liu, D. Hong, P. Ghamisi, Cascaded recurrent neural networks for hyperspectral image classification. IEEE Geosci. Remote Sens. 57(8), 5384–5394 (2019). https://doi.org/10.1109/TGRS.2019.2899129CrossRef

24.

W. Hu, Y. Huang, L. Wei, F. Zhang, H. Li, Deep convolutional neural networks for hyperspectral image classification. J. Sens. 2015, 1–12 (2015)CrossRef

25.

J. Yang, Y. Zhao, J.C. Chan, C. Yi, Hyperspectral image classification using two-channel deep convolutional neural network, in Proceedings of the IEEE IGARSS (2016), pp. 5079–5082

26.

Y. Li, H. Zhang, Q. Shen, Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1), 67 (2017)CrossRef

27.

D. Hong, N. Yokoya, G. Xia, J. Chanussot, X. Zhu, X-ModalNet: a semi-supervised deep cross-modal network for classification of remote sensing data. ISPRS J. Photogram. Remote Sens. 167, 12–23 (2020)CrossRef

28.

C. Wang, S. Pan, R. Hu, G. Long, J. Jiang, C. Zhang, Attributed graph clustering: a deep attentional embedding approach. in Proceedings of the International Joint Conference on Artificial Intelligent (IJCAI) (2019), pp. 3670–3676

29.

D. Hong, N. Yokoya, J. Chanussot, J. Xu, X. Zhu, Learning to propagate labels on graphs: an iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction. ISPRS J. Photogram. Remote Sens 158, 35–49 (2019)CrossRef

30.

Z. Zhang, P. Cui, W. Zhu, Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. 32, 1–20 (2020)

31.

T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in Proceedings of the ICLR (2017)

32.

Z. Zhang, P. Cui, W. Zhu, Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. 45, 1–20 (2020)

33.

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 (2019). https://doi.org/10.1109/LGRS.2018.2869563CrossRef

34.

A. Sha, B. Wang, X. Wu, L. Zhang, Semisupervised classification for hyperspectral images using graph attention networks. IEEE Geosci. Remote Sens. Lett. 8, 23 (2020)

35.

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 (2020)CrossRef

36.

Y. Ding, X. Zhao, Z. Zhang, W. Cai, N. Yang, Multi-scale 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). https://doi.org/10.1109/JSTARS.2021.3074469CrossRef

37.

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, 5966–5978 (2020). https://doi.org/10.1109/TGRS.2020.3015157CrossRef

38.

T. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in International Conference on Learning Representations (2017)

39.

J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral networks and locally connected networks on graphs, in Proceedings of the International Conference on Learning Representive (CLR) (2014), pp. 1–14

40.

A. Sandryhaila, J.M.F. Moura, Discrete signal processing on graphs. IEEE Trans. Signal Process. 61(7), 1644–1656 (2013)MathSciNetCrossRef

41.

D.I. Shuman, S.K. Narang, P. Frossard, A. Ortega, P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Sig. Process. Mag. 30(3), 83–98 (2013)CrossRef

42.

P.A. Gagniuc, Markov Chains: From Theory to Implementation and Experimentation (Wiley, 2017)

43.

S. Bernhard, A. Smola, and K-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural comput 10(5):1299–1319 (1998)

44.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Strunk, Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef

45.

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. 15, 1–12 (2020)

46.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNet

47.

S. Zhang, S. Li, Spectral-spatial classification of hyperspectral images via multiscale superpixels based sparse representation, in Proceedings of the IEEE IGARSS (2016), pp. 2423–2426

Titel: Multi-receptive Field: An Adaptive Path Aggregation Graph Neural Framework for Hyperspectral Image Classification
verfasst von: Yao Ding
Zhili Zhang
Haojie Hu
Fang He
Shuli Cheng
Yijun Zhang
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_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"