Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
12. Internationaler Motorenkongress 2025 | 25. + 26. Februar 2025

Treffpunkt der Community für Antriebe und nachhaltige Kraftstoffe

Der Kongress beleuchtet die zukünftige Rolle des Verbrennungsmotors und seiner nichtfossilen Kraftstoffe aus globaler Sicht mit führenden Experten. Seien Sie dabei! Weitere Informationen und Buchungsmöglichkeiten finden Sie auf unserer Website.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

8. Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering

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
loading …

Abstract

Hyperspectral imagery (HSI) acquired by remote sensing system are composed of hundreds of contiguous and narrow spectral bands with abundant spatial-spectral information in the electromagnetic spectrum (Liu et al. in IEEE Trans Neural Netw Learn Syst 34:8989–9003, 2022; Gao et al. in IEEE Trans Geosci Remote Sens 60:1–15, 2022; Ding et al. in IEEE Geosci Remote Sens Lett 19:1–5, 2022). Due to its unique advantages, HSI has attracted lots of attention and has been widely applied in various fields, including military reconnaissance, urban mapping, biochemical detection, forest fire detection, and target recognition (Ding et al. in IEEE J Select Top Appl Earth Observ Remote Sens 14:4561–4572, 2021; Ding et al. in IEEE Trans Geosci Remote Sens 60:1–12, 2022; Li et al. in IEEE Trans Neural Netw Learn Syst 34:8057–8070, 2022), etc.

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 S2GFormer: Exploring Relationship Between Transformer and Graph Convolution for Hyperspectral Image Classification
Literatur
1.
H. Liu, W. Li, X. Xia, M. Zhang, C. Gao, R. Tao, Central attention network for hyperspectral imagery classification. IEEE Trans. Neural Netw. Learn. Syst. 34, 8989–9003 (2022)CrossRef
2.
Y. Gao, W. Li, M. Zhang, J. Wang, W. Sun, R. Tao, Q. Du, Hyperspectral and multispectral classification for coastal wetland using depthwise feature interaction network. IEEE Trans. Geosci. Remote Sens.Geosci. Remote Sens. 60, 1–15 (2022)
3.
4.
5.
6.
W. Li, Y. Gao, M. Zhang, R. Tao, Q. Du, Asymmetric feature fusion network for hyperspectral and SAR image classification. IEEE Trans. Neural Netw. Learn. Syst. 34, 8057–8070 (2022)CrossRef
7.
Y. Zhang, W. Li, R. Tao, J. Peng, Q. Du, Z. Cai, Cross-scene hyperspectral image classification with discriminative cooperative alignment. IEEE Trans. Geosci. Remote Sens.Geosci. Remote Sens. 59(11), 9646–9660 (2021)CrossRef
8.
9.
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.Geosci. Remote Sens. 59(5), 4191–4202 (2021)CrossRef
10.
R. Vidal, Subspace clustering. IEEE Sig. Process. Mag. 28(2), 52–68 (2011)CrossRef
11.
J. Lei, X. Li, B. Peng, L. Fang, N. Ling, Q. Huang, Deep spatialspectral subspace clustering for hyperspectral image. IEEE Trans. Circ. Syst. Video Technol. 31(7), 2686–2697 (2021)CrossRef
12.
X. Peng, J. Feng, J.T. Zhou, Y. Lei, S. Yan, Deep subspace clustering. IEEE Trans. Neural Netw. Learn. Syst. 31(12), 5509–5521 (2020)MathSciNetCrossRef
13.
X. Yang, G. Lin, Y. Liu, F. Nie, L. Lin, Fast spectral embedded clustering based on structured graph learning for large-scale hyperspectral image. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022)
14.
R. Wang, F. Nie, W. Yu, Fast spectral clustering with anchor graph for large hyperspectral images. IEEE Geosci. Remote Sens. Lett. 14(11), 2003–2007 (2017)CrossRef
15.
T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, A.Y. Wu, An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRef
16.
A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, in Proceedings of the Advance Neural Information Processing System (NIPS) (2002), pp. 849–856
17.
T. Yang, C. Lee, S. Yen, Fuzzy objective functions for robust pattern recognition, in Proceedings of the 2009 IEEE International Conference on Fuzzy Systems (2009), pp. 2057–2062
18.
C.A. Shah, M.K. Arora, P.K. Varshney, Unsupervised classification of hyperspectral data: an ICA mixture model-based approach. Int. J. Remote Sens. 25(2), 481–487 (2004)CrossRef
19.
Y. Zhang, W. Li, M. Zhang, Y. Qu, R. Tao, H. Qi, Topological structure and semantic information transfer network for cross-scene hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst. (2022)
20.
J. Lin, C. He, Z.J. Wang, S. Li, Structure preserving transfer learning for unsupervised hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 14(10), 1656–1660 (2017)CrossRef
21.
J.M. Murphy, M. Maggioni, Unsupervised clustering and active learning of hyperspectral images with nonlinear diffusion. IEEE Trans. Geosci. Remote Sens. 57(3), 1829–1845 (2019)CrossRef
22.
E. Elhamifar, R. Vidal, Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)CrossRef
23.
X. Peng, H. Tang, L. Zhang, Z. Yi, S. Xiao, A unified framework for representation-based subspace clustering of out-of-sample and largescale data. IEEE Trans. Neural Netw. Learn. Syst. 27(12), 2499–2512 (2016)MathSciNetCrossRef
24.
J. Fan, T. Chen, S. Lu, Superpixel guided deep-sparse-representation learning for hyperspectral image classification. IEEE Trans. Circ. Syst. Video Technol. 28(11), 3163–3173 (2018)CrossRef
25.
S. Matsushima, M. Brbic, Selective sampling-based scalable sparse subspace clustering, in Advances in Neural Information Processing Systems, by eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alche-Buc, E. Fox, R. Garnett (Curran Associates, Inc., 2019)
26.
H. Zhai, H. Zhang, L. Zhang, P. Li, Total variation regularized collaborative representation clustering with a locally adaptive dictionary for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 57(1), 166–180 (2018)CrossRef
27.
S. Chen, D. Zhang, Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. B 34(4), 1907–1916 (2004)CrossRef
28.
Y. Ding, Z. Zhang, X. Zhao, D. Hong, W. Li, W. Cai, Y. Zhan, AF2GNN: graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification, in Information Sciences (2022)
29.
F. Nie, X. Wang, M. Jordan, H. Huang, The constrained Laplacian rank algorithm for graph-based clustering, in Proceedings of the AAAI Conference on Artificial Intelligence (2016)
30.
J. Xie, R. Girshick, A. Farhadi, Unsupervised deep embedding for clustering analysis, in Proceedings of the 33rd International Conference on Machine Learning, Series Proceedings of Machine Learning Research, by eds. M.F. Balcan, K.Q. Weinberger (PMLR, New York, 2016), pp. 478–487
31.
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
32.
Y. Cai, Z. Zhang, P. Ghamisi, Y. Ding, X. Liu, Z. Cai, R. Gloaguen, Superpixel contracted neighborhood contrastive subspace clustering network for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022)
33.
J. Lei, X. Li, B. Peng, L. Fang, N. Ling, Q. Huang, Deep spatial-spectral subspace clustering for hyperspectral image. IEEE Trans. Circ. Syst. Video Technol. 31(7), 2686–2697 (2021)CrossRef
34.
S. Hou, H. Shi, X. Cao, X. Zhang, L. Jiao, Hyperspectral imagery classification based on contrastive learning. IEEE Trans. Geosci. Remote Sens. (2021)
35.
Y. Li, P. Hu, Z. Liu, D. Peng, J.T. Zhou, X. Peng, Contrastive clustering, in Proceedings of the 2021 AAAI Conference on Artificial Intelligence (AAAI) (2021), pp. 8547–8555
36.
J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral Networks and Locally Connected Networks on Graphs (2013)
37.
T. Kipf, M. Welling, Variational Graph Auto-Encoders (2016)
38.
T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, in Proceedings of the ICLR (2017)
39.
P. Veličković, G. Cucurull, A. Casanova, et al., Graph Attention Networks (2017)
40.
C. Wang, S. Pan, G. Long, X. Zhu, J. Jing, Mgae: marginalized graph autoencoder for graph clustering, in Proceedings of the 2017 ACM (2017)
41.
J. Park, M. Lee, H.J. Chang, K. Lee, J. Choi, Symmetric graph convolutional autoencoder for unsupervised graph representation learning, in Proceedings of the IEEE International Conference on Computer Vision (2019), pp. 6519–6528
42.
X. Zhang, H. Liu, Q. Li, X. Wu, Attributed graph clustering via adaptive graph convolution, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (AAAI Press, 2019), pp. 4327–4333
43.
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, C. Zhang, Adversarially regularized graph autoencoder for graph embedding. IJCAI 12, 2609–2615 (2018)
44.
R. Horn, C. Johnson, Matrix Analysis (Cambridge university press, 2012)
45.
J. Xie, R. Girshick, A. Farhadi, Unsupervised deep embedding for clustering analysis, in ICML (PMLR, New York, 2016), pp. 478–487
46.
B. Scholkopf, A. Smola, K.-R. Muller, Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)CrossRef
47.
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
48.
G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRef
Metadaten
Titel
Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering
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_8

Premium Partner

    Bildnachweise