Abstract
Band selection (BS) is an effective approach to alleviate the curse of dimensionality in hyperspectral image (HSI). Despite the plethora of BS methods proposed, two critical issues persist. Firstly, many approaches fail to integrate local and global band characteristics when describing their adjacencies. Secondly, HSI is typically treated as a whole in spatial information extraction, ignoring the differences of spatial structure inherent in each band. To address these issues, this article proposes a novel spectral-spatial multi-view sparse self-representation model for hyperspectral BS. Firstly, a dynamic grouping strategy is designed to partition bands by incorporating the local and global adjacencies, promoting intra-group coherence and inter-group disparity. Accordingly, metrics such as local density, the difference between intergroup and information entropy are combined to evaluate band significance within each group, and ultimately selecting bands with high information and low redundancy to constitute a feature band subset. Secondly, a series of spatial similarity graphs of the feature bands is constructed to capture the spatial structure differences across multiple views. Simultaneously, a weighted adaptive multi-graph fusion strategy is developed to leverage the strengths of these graphs, yielding a unified similarity graph. This approach effectively exploits both local and global band adjacencies, so as to capture the spatial distribution differences of ground objects more precisely. Finally, experiments on two public datasets demonstrate the superiority of the proposed model.
This work was supported in part by Qingdao Natural Science Foundation Grant 23-2-1-64-zyyd-jch, China Postdoctoral Science Foundation Grant 2023M731843, Postdoctoral Applied Research Foundation of Qingdao under Grant QDBSH20230101012, National Natural Science Foundation of China under Grant 42301380, Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China under Grant 2023KJ232.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shang, X., Song, M., Wang, Y., Yu, C., Yu, H., Li, F., Chang, C.I.: Target-constrained interference-minimized band selection for hyperspectral target detection. IEEE Trans. Geosci. Remote Sens. 59(7), 6044–6064 (2021)
Chang, C.I., Du, Q., Sun, T.-L.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37(6), 2631–2641 (1999)
Geng, X., Sun, K., Ji, L., Zhao, Y.: A fast volume-gradient-based band selection method for hyperspectral image. IEEE Trans. Geosci. Remote Sens. 52(11), 7111–7119 (2014)
Agarwal, A., El-Ghazawi, T., El-Askary, H., Le-Moigne, J.: Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. IEEE Int. Symp. Signal Process. Inf. Technol. 353–356 (2007)
Su, H., Cai, Y., Du, Q.: Firefly-algorithm-inspired framework with band selection and extreme learning machine for hyperspectral image classification. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 10(1), 309–320 (2017)
Chang, C.I., Du, Q., Sun, T.L., Althouse, M.L.G.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37(6), 2631–2641 (1999)
Li, W., Prasad, S., Fowler, J.E., Bruce, L.M.: Locality-preserving dimensionality reduction and classification for hyperspectral image analysis. IEEE Trans. Geosci. Remote Sens. 50(4), 1185–1198 (2012)
Bandos, T.V., Bruzzone, L., Gustavo, C.V.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans. Geosci. Remote Sens. 47(3), 862–873 (2009)
Feng, J., Jiao, L., Liu, F., Sun, T., Zhang, X.: Mutual-information-based semi-supervised hyperspectral band selection with high discrimination, high information, and low redundancy. IEEE Trans. Geosci. Remote Sens. 53(5), 2956–2969 (2015)
Zhang, X., He, Y., Zhou, N., Zheng, Y.: Semisupervised dimensionality reduction of hyperspectral images via local scaling cut criterion. IEEE Geosci. Remote Sens. Lett. 10(6), 1547–1551 (2013)
Jia, S., Ji, Z., Qian, Y., Shen, L.: Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 5(2), 531–543 (2012)
Sun, W., Zhang, L., Zhang, L., Lai, M.Y.: A dissimilarity weighted sparse self-representation method for band selection in hyperspectral imagery classification. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 9(9), 4374–4388 (2016)
Li, S., Qi, H.: Sparse representation based band selection for hyperspectral images. IEEE Int. Conf. Image Process. 2693–2696 (2011)
Wei, X., Zhu, W., Liao, B., Cai, L.: Scalable one-pass self-representation learning for hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 57(7), 4360–4374 (2019)
Tang, C., Wang, J., Zheng, X., Liu, X., Xie, W., Li, X., Zhu, X.: Spatial and spectral structure preserved self-representation for unsupervised hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 61, 1–13 (2023)
Liu, M.Y., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 20–25. IEEE Computer Society, Colorado Springs, USA (2011)
Sui, C., Zhou, J., Li, C., Zhang, Q., Feng, J., Mei, X., Wang, J.: Unsupervised hyperspectral band selection with multigraph integrated embedding and robust self-contained regression. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022)
Chang, C.I., Du, Q., Sun, T.L., Althouse, M.L.G.: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37(6), 2631–2641 (1999)
Jia, S., Tang, G., Zhu, J., Li, Q.: A novel ranking-based clustering approach for hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 54(1), 88–102 (2016)
Wang, Q., Zhang, F., Li, X.: Optimal clustering framework for hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 56(10), 5910–5922 (2018)
Wang, Q., Li, Q., Li, X.: Hyperspectral band selection via adaptive subspace partition strategy. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 12(12), 4940–4950 (2019)
Cai, Y., Liu, X., Cai, Z.: BS-nets: an end-to-end framework for band selection of hyperspectral image. IEEE Trans. Geosci. Remote Sens. 58(3), 1969–1984 (2020)
Cai, Y., Zhang, Z., Liu, X., Cai, Z.: Efficient graph convolutional self-representation for band selection of hyperspectral image. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 13, 4869–4880 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Fu, B., Zhang, J., Shang, X. (2025). Spectral-Spatial Multi-view Sparse Self-Representation for Hyperspectral Band Selection. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15043. Springer, Singapore. https://doi.org/10.1007/978-981-97-8493-6_25
Download citation
DOI: https://doi.org/10.1007/978-981-97-8493-6_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-8492-9
Online ISBN: 978-981-97-8493-6
eBook Packages: Computer ScienceComputer Science (R0)