Abstract
Band selection is an effective method to reduce the dimensionality of hyperspectral data and has become a research hotspot in the field of hyperspectral image analysis. However, existing BS methods mostly rely on a single band prioritization, resulting in incomplete band evaluation. Furthermore, many BS methods use generic criteria without considering the spectral characteristics of specific targets, leading to poor application capabilities of the selected bands in target detection tasks. Therefore, this paper proposes an innovative target-oriented MCBS method for selecting the most suitable detection bands for specific objectives. Firstly, based on several baseline criteria proposed in this study, a standard decision matrix is constructed to evaluate the bands from multiple perspectives, forming a target-oriented band priority sequence. Then the method divides the original data into subspaces with low correlation and select the highest-scoring band from each subspace to form the band subset. Thus, a low-correlation subset of bands specifically designed for object detection is obtained. Experimental results on three datasets demonstrate that the proposed method outperforms several widely used cutting-edge BS methods in terms of target detection.
This work is supported by National Natural Science Foundation of China (No. 62002041 and 62176037), Dalian Science and Technology Bureau (No. 2021JJ12GX028 and 2022JJ12GX019).
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
Zhang, L., Zhang, L., Tao, D., Huang, X., Du, B.: Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans. Geosci. Remote Sens. 52(8), 4955–4965 (2013)
Sun, X., Zhu, Y., Fu, X.: RGB and optimal waveband image fusion for real-time underwater clear image acquisition. IEEE Trans. Instrum. Meas., 1 (2023)
Feng, L., Meng, X., Wang, H.: Multi-view locality low-rank embedding for dimension reduction. Knowl.-Based Syst. 191, 105172 (2020)
Jiang, G., Wang, H., Peng, J., Chen, D., Fu, X.: Graph-based multi-view binary learning for image clustering. Neurocomputing 427, 225–237 (2021)
Yang, H., Du, Q., Su, H., Sheng, Y.: An efficient method for supervised hyperspectral band selection. IEEE Geosci. Remote Sens. Lett. 8(1), 138–142 (2010)
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 (2014)
Zhang, M., Ma, J., Gong, M.: Unsupervised hyperspectral band selection by fuzzy clustering with particle swarm optimization. IEEE Geosci. Remote Sens. Lett. 14(5), 773–777 (2017)
Wang, H., Yao, M., Jiang, G., Mi, Z., Fu, X.: Graph-collaborated auto-encoder hashing for multiview binary clustering. IEEE Trans. Neural Netw. Learn. Syst. (2023)
Wang, H., Feng, L., Meng, X., Chen, Z., Yu, L., Zhang, H.: Multi-view metric learning based on KL-divergence for similarity measurement. Neurocomputing 238, 269–276 (2017)
Chang, C.I., Wang, S.: Constrained band selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 44(6), 1575–1585 (2006)
Chang, C.I., Du, Q., Sun, T.L., Althouse, M.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)
Wang, Q., Li, Q., Li, X.: Hyperspectral band selection via adaptive subspace partition strategy. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 12(12), 4940–4950 (2019)
Sun, X., Shen, X., Pang, H., Fu, X.: Multiple band prioritization criteria-based band selection for hyperspectral imagery. Remote Sens. 14(22), 5679 (2022)
Ji, H., Zuo, Z., Han, Q.L.: A divisive hierarchical clustering approach to hyperspectral band selection. IEEE Trans. Instrum. Meas. 71, 1–12 (2022)
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 (2015)
Wang, Q., Zhang, F., Li, X.: Optimal clustering framework for hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 56(10), 5910–5922 (2018)
Sun, X., Zhang, H., Xu, F., Zhu, Y., Fu, X.: Constrained-target band selection with subspace partition for hyperspectral target detection. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 14, 9147–9161 (2021)
Wang, J., Wang, H., Ma, Z., Wang, L., Wang, Q., Li, X.: Unsupervised hyperspectral band selection based on hypergraph spectral clustering. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
Das, S., Pratiher, S., Kyal, C., Ghamisi, P.: Sparsity regularized deep subspace clustering for multicriterion-based hyperspectral band selection. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 15, 4264–4278 (2022)
Yu, C., Lee, L.C., Chang, C.I., Xue, B., Song, M., Chen, J.: Band-specified virtual dimensionality for band selection: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 56(5), 2822–2832 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pang, H., Sun, X., Fu, X., Wang, H. (2024). Target-Oriented Multi-criteria Band Selection for Hyperspectral Image. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14431. Springer, Singapore. https://doi.org/10.1007/978-981-99-8540-1_33
Download citation
DOI: https://doi.org/10.1007/978-981-99-8540-1_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8539-5
Online ISBN: 978-981-99-8540-1
eBook Packages: Computer ScienceComputer Science (R0)