Abstract
In order to reduce the spectral redundancy of hyperspectral remote sensing images and reduce the computational complexity of subsequent processing, an unsupervised hyperspectral image band selection algorithm based on low-rank representation (LRBS) was proposed in this paper. First, a low-rank representation of the hyperspectral image is proposed and a low-rank coefficient matrix is obtained. Then, each column of the low-rank coefficient is used as a vertex of the graph to perform spectral clustering. Lastly, we use the fixed initial k-means cluster centers for clustering to get the salient band of each cluster. The experimental simulation results show that the bands selected by LRBS algorithm can improve the classification accuracy and have better performance than other methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lu, X., Zhang, W., Li, X.: A hybrid sparsity and distance-based discrimination detector for hyper-spectral images. IEEE Trans. Geosci. Remote Sens. PP(99), 1–14 (2018)
Song, Meiping, Chang, Chein-I: A theory of recursive orthogonal subspace projection for hyper-spectral imaging. IEEE Trans. Geosci. Remote Sens. 53(6), 3055–3072 (2015)
Song, M., Chen, S.-Y., Li, H.-C., Chen, H.-M., Chen, C.C.-C, Chein-I, C.: Finding virtual signatures for linear spectral mixture analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2704–2719 (2015)
UI Haq, I., Xu, X., Shahzad, A.: Band clustering and selection and decision fusion for target detection in hyperspectral imagery. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan,April 19–24, pp. 110 l–1104 (2009)
Chunyan, Yu., Chang, Li-Chien Lee Chein-I, Xue, Bai, Song, Meiping, Chen, Jian: BanSpecified virtual dimensionality for band selection: an orthogonal subspace projection approach. IEEE Trans. Geosc. Remote Sens. 56(5), 2822–2832 (2018)
Sun, W., Jiang, M., Li, W., Liu, Y.: A symmetric sparse representation based bandselction method for Hyperspectral imagery classification. Remote Sens. 8, 238 (2016)
Su, H., Du, Q., Chen, G., et al.: Optimized Hyperspectral band selection using particle swarm optimization[j]. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2659–2670 (2014)
Sumarsono, A., Du, Q.: Low-Rank subspace representation for supervised and unsupervised classification of Hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. PP(99), 1–8 (2016)
Lu, X., Chen, Y., Li, X.: Hierarchical recurrent neural hashing for image retrieval with hierarchical convolutional features. IEEE Trans. Image Proces. PP(99), 1 (2017)
Wang, Y., Lee, L.C., Xue, B., et al.: A posteriori Hyperspectral anomaly detection for unlabeled classification. IEEE Trans. Geosci. Remote Sens., 1–16 (2018)
Wang, X., Liu, F.: weighted low-rank representation-based dimension reduction for Hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 14(11), 1938–1942 (2017)
Cai, J.-F., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix comple-tion. SIAM J. Optim. 20(4)
Acknowledgments
The work of C. Yu is supported by National Nature Science Foundation of Liaoning Province (20170540095) and Fundamental Research Funds for the Central Universities (3132018196).
The work of C.-I Chang is supported by the Fundamental Research Funds for Central Universities under Grant (3132016331).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yu, C., Cen, K., Chang, CI., Li, F. (2019). Unsupervised Hyperspectral Band Selection Method Based on Low-Rank Representation. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-13-6264-4_124
Download citation
DOI: https://doi.org/10.1007/978-981-13-6264-4_124
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6263-7
Online ISBN: 978-981-13-6264-4
eBook Packages: EngineeringEngineering (R0)