Abstract
Hundreds of continuous bands make up a hyperspectral image. All the bands are not equal important. Some of the bands are significant and others are redundant. Band reduction is a typical step before further processing. Instead of attempting to handle the complete information set without losing crucial data, it is essential to select the most valuable bands. Using traditional band selection techniques, the predetermined number of dimensions are selected from the hyperspectral image. In this article, we propose a novel single-layer neural network and a genetic evolutionary approach to reduce a hyperspectral image’s high dimension. The process involves selecting the two bands with the lowest correlation in each iteration and eliminating two redundant bands. The suggested framework eliminates the unnecessary bands from a hyperspectral image and then chooses the ideal number of the most crucial bands.
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
Agarwal, A., El-Ghazawi, T., El-Askary, H., Le-Moigne, J.: Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. In: 2007 IEEE International Symposium on Signal Processing and Information Technology, pp. 353–356 (2007)
Ahmad, M., Khan, A.M., Brown, J.A., Protasov, S., Khattak, A.M.: Gait fingerprinting-based user identification on smartphones. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3060–3067 (2016a)
Ahmad, M., Khan, A.M., Hussain, R., Protasov, S., Chow, F., Khattak, A.M.: Unsupervised geometrical feature learning from hyperspectral data. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–6 (2016b)
Ahmad, M., Protasov, S., Khan, A.: Hyperspectral band selection using unsupervised non-linear deep auto encoder to train external classifiers. Unknown (2017)
Ahmad, M., Ulhaq, D., Mushtaq, Q.: AIK method for band clustering using statistics of correlation and dispersion matrix, pp. 114–118 (2011)
Brown, A., Sutter, B., Dunagan, S.: The MARTE VNIR imaging spectrometer experiment: design and analysis. Astrobiology 8, 1001–11 (2008)
Chang, C.-I.: Techniques for spectral detection and classification. In: Hyperspectral Imaging (2003)
Chang, C.-I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 42(3), 608–619 (2004)
Chang, C.-I., Wang, S.: Constrained band selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 44(6), 1575–1585 (2006)
Hidalgo, D.R., Cortés, B.B., Bravo, E.C.: Dimensionality reduction of hyperspectral images of vegetation and crops based on self-organized maps. Inf. Process. Agric. 8(2), 310–327 (2021)
Du, Q., Yang, H.: Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci. Remote Sens. Lett. 5(4), 564–568 (2008)
Estevez, P.A., Tesmer, M., Perez, C.A., Zurada, J.M.: Normalized mutual information feature selection. IEEE Trans. Neural Networks 20(2), 189–201 (2009)
Mathieu, F., Jocelyn, C., Benediktsson, J.A.: Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP J. Adv. Signal Process. 2009, 783194 (2009)
Feng, J., Jiao, L.C., Zhang, X., Sun, T.: Hyperspectral band selection based on trivariate mutual information and clonal selection. IEEE Trans. Geosci. Remote Sens. 52(7), 4092–4105 (2014)
Gustavsson, D., Wadströmer, N.: Non-linear hyperspectral subspace mapping using stacked auto-encoder (2016)
He, X., Cai, D., Yan, S., Zhang, H.-J.: Neighborhood preserving embedding. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 1, 2, pp. 1208–1213 (2005)
Ifarraguerri, A., Prairie, M.: Visual method for spectral band selection. IEEE Geosci. Remote Sens. Lett. 1(2), 101–106 (2004)
Feng, J., Licheng Jiao, F.L.T.S.X.Z.: Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recogn. 51, 295–309 (2016)
Kanning, M., Siegmann, B., Jarmer, T.: Regionalization of uncovered agricultural soils based on organic carbon and soil texture estimations. Remote Sens. 8(11), 927 (2016)
Karaca, A.C., Güllü, M.K.: Comparison of traditional and recent unsupervised band selection approaches in hyperspectral images. In: 2016 24th Signal Processing and Communication Application Conference (SIU), pp. 785–788 (2016)
Li, W., Prasad, S., Fowler, J.E., Bruce, L.M.: Locality-preserving discriminant analysis in kernel-induced feature spaces for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 8(5), 894–898 (2011)
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)
Liu, L., Li, C.F., Lei, Y.M., et al.: Feature extraction for hyperspectral remote sensing image using weighted PCA-ICA, vol. 10 (2017)
MartÍnez-UsÓMartinez-Uso, A., Pla, F., Sotoca, J.M., GarcÍa-Sevilla, P.: Clustering-based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2007)
Nahr, S.T., Pahlavani, P., Hasanlou, M.: Different optimal band selection of hyperspectral images using a continuous genetic algorithm. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40(2), 249 (2014)
Nakamura, R.Y.M., Fonseca, L.M.G., dos Santos, J.A., da S. Torres, R., Yang, X.-S., Papa, J.P.: Nature-inspired framework for hyperspectral band selection. IEEE Trans. Geosci. Remote Sens. 52(4), 2126–2137 (2014)
Patra, S., Modi, P., Bruzzone, L.: Hyperspectral band selection based on rough set. IEEE Trans. Geosci. Remote Sens. 53(10), 5495–5503 (2015)
Paul Arati, C.N.: Dimensionality reduction of hyperspectral images using pooling. Pattern Recognit Image Anal. 29, 72–78 (2019)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–6 (2000)
Shuaibu, M., Lee, W.S., Schueller, J., Gader, P., Hong, Y.K., Kim, S.: Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Comput. Electron. Agric. 148, 45–53 (2018)
Shukla, U.P., Nanda, S.J.: A binary social spider optimization algorithm for unsupervised band selection in compressed hyperspectral images. Expert Syst. Appl. 97, 336–356 (2018)
Solorio-Fernández, S., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: Hybrid feature selection method for supervised classification based on Laplacian score ranking. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 260–269. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15992-3_28
Su, H., Du, Q., Chen, G., Du, P.: Optimized hyperspectral band selection using particle swarm optimization. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 7(6), 2659–2670 (2014)
Su, H., Yong, B., Du, Q.: Hyperspectral band selection using improved firefly algorithm. IEEE Geosci. Remote Sens. Lett. 13(1), 68–72 (2016)
Wang, Q., Meng, Z., Li, X.: Locality adaptive discriminant analysis for spectral-spatial classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 14(11), 2077–2081 (2017)
Xie, F., Li, F., Lei, C., Yang, J., Zhang, Y.: Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification. Appl. Soft Comput. 75, 428–440 (2019)
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 (2011)
Yang, R., Su, L., Zhao, X., Wan, H., Sun, J.: Representative band selection for hyperspectral image classification. J. Vis. Commun. Image Represent. 48, 396–403 (2017)
Yuan, Y., Zhu, G., Wang, Q.: Hyperspectral band selection by multitask sparsity pursuit. IEEE Trans. Geosci. Remote Sens. 53(2), 631–644 (2015)
Zhang, L., Zhong, Y., Huang, B., Gong, J., Li, P.: Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 45(12), 4172–4186 (2007)
Zhang, Y., Desai, M., Zhang, J., Jin, M.: Adaptive subspace decomposition for hyperspectral data dimensionality reduction. In: Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), vol. 2, pp. 326–329 (1999)
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 Switzerland AG
About this paper
Cite this paper
Bar, R.K., Mukhopadhyay, S., Chakraborty, D., Hinchey, M. (2024). Dimension Reduction in Hyperspectral Image Using Single Layer Perceptron Neural Network. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-48876-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48875-7
Online ISBN: 978-3-031-48876-4
eBook Packages: Computer ScienceComputer Science (R0)