Abstract
Hyperspectral images captured through the hyperspectral sensors play an imperative part in remote sensing applications in the present context. Unlike traditional images sensed with few bands in the visible spectrum, the hyperspectral (HS) images are obtained with hundreds of spectral band ranges from infrared to ultraviolet regions. Because of its vast spatial and spectral data, it requires an extensive computational system for processing and its hidden features are needed to be unveiled in an effective manner specifically for the classification of HS imagery. This approach exploits the high spectral band correlation and rich spatial information of the HS images for the generation of feature vectors. To attain optimal feature space for the best probable classification, an adaptive approach is incorporated to adaptively choose spectral–spatial features for feature selection to classify the pixels effectively. Furthermore, the HS image encompasses several bands including noisy bands. To categorize the images with great accuracy, it is suggested to eradicate the noisy bands whilst retaining the informative bands. In this research, an adaptive spectral–spatial feature selection scheme is proposed for HS images where the extremely correlated representative bands are considered for analysis with uncorrelated and noisy spectral bands are judiciously discarded during its classification process. This hybrid approach not merely diminishes the computational time and also improves the general classification accuracy significantly. The empirical result displays that the proposed work surpasses the conventional approach of HS image classification systems.
Similar content being viewed by others
References
Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction, 4th edn. Springer, Berlin (2006)
Ji, R., Gao, Y., Hong, R., Liu, Q., Tao, D., Li, X.: Spectral–spatial constraint hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 52(3), 1811–1824 (2014)
Pu, H., Chen, Z., Wang, B., Jiang, G.-M.: A novel spatial–spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 52(11), 7008–7022 (2014)
Wang, K., Yong, B., Gu, X., Xiao, P., Zhang, X.: Spectral similarity measure using frequency spectrum for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 12(1), 130–134 (2015)
Zhang, E., Zhang, X., Wang, S.: Improving hyperspectral image classification using spectral information divergence. IEEE Geosci. Remote Sens. Lett. 11(1), 249–253 (2014)
Jiao, H., Zhong, Y., Zhang, L.: An unsupervised spectral matching classifier based on artificial DNA computing for hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 52(8), 4524–4538 (2014)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)
Khodadadzadeh, M., Li, J., Plaza, A., Ghassemian, H., Bioucas-Dias, J.M., Li, X.: Spectral–spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Trans. Geosci. Remote Sens. 52(10), 6298–6314 (2014)
Kang, X., Li, S., Benediktsson, J.A.: Spectral–spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 52(5), 2666–2677 (2014)
Li, J., Huang, X., Gamba, P., Bioucas-Dias, J.M., Zhang, L., Benediktsson, J.A., Plaza, A.: Multiple feature learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 53(3), 1592–1606 (2015)
Chidambaram, S., Sumathi, A.: A novel spectral signature based classification approach for airborne and spaceborne hyperspectral imagery. Asian J. Inf. Technol. 15(23), 4926–4933 (2016)
Landgrebe, D.: Hyperspectral image data analysis. IEEE Signal Process. Mag. 19(1), 17–28 (2002)
Plaza, A., Benediktsson, J.A., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, J.A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113(Supplement 1), S110–S122 (2009)
Maggiori, E., Tarabalka, Y., Charpiat, G.: Improved partition trees for multi-class segmentation of remote sensing images. In: IEEE IGARSS, pp. 1016–1019 (2015)
Li, Y., Zhang, H., Shen, Q.: Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9, 67 (2017). https://doi.org/10.3390/rs9010067
Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 43(6), 1351–1362 (2005)
Gualtieri, J.A., Cromp, R.F.: Support vector machines for hyperspectral remote sensing classification. Proc. SPIE 3584, 221–232 (1998)
Fauvel, M., Chanussot, J., Benediktsson, J.A.: Evaluation of kernelsfor multiclass classification of hyperspectral remote sensing data. In: Proceedings of ICASSP, May 2006, pp. II-813–II-816 (2006)
BalaAnand, M., Karthikeyan, N., Karthik, S.: Designing a framework for communal software: based on the assessment using relation modelling. Int. J. Parallel Program. (2018). https://doi.org/10.1007/s10766-018-0598-2
Vapnik, V.N.: Statistical Learning Theory, vol. 2. Wiley, New-York (1998)
Lin, Y.: Support vector machines and the Bayes rule in classification. Data Min. Knowl. Discov. 6, 259 (2002). https://doi.org/10.1023/A:1015469627679
Marconcini, M., Camps-Valls, G., Bruzzone, L.: A composite semisupervised SVM for classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 6(2), 234–238 (2009)
Tarabalka, Y., Benediktsson, J.A., Chanussot, J.: Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. Geosci. Remote Sens. 47(8), 2973–2987 (2009)
Archibald, R., Fann, G.: Feature selection and classification of hyperspectral images with support vector machines. IEEE Geosci. Remote Sens. Lett. 4(4), 674–677 (2007)
Cortes, C., Vapnik, V.: Support-vector network. Mach. Learn. 20, 1–25 (1995)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Berlin (1998)
Fang, L., Li, S., Duan, W., Ren, J., Benediktsson, J.A.: Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels. IEEE Trans. Geosci. Remote Sens. 53(12), 6663–6674 (2015)
Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: k-nearest neighbor classification. In: Pardalos, P.M. (ed.) Data Mining in Agriculture. Springer Optimization and Its Applications, vol. 34. Springer, New York (2009). https://doi.org/10.1007/978-0-387-88615-2_4
Kim, J., Kim, B.-S., Savarese, S.: 6th WSEAS International Conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, pp. 133–138. Cambridge, Harvard, 25–27 January 2012. ISBN: 978-1-61804-064-0
AVIRIS Salinas Valley Hyperspectral Datasets. http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes. Accessed 19 Jan 2018
Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)
Devi, G., Chauhan, C., Chakraborti, S.: Proceedings of the Sixth Symposium on Educational Advances in Artificial Intelligence (EAAI-16), pp. 4075–4080
Zebin, W., Wang, Q., Plaza, A., Li, J., Sun, L., Wei, Z.: Parallel spatial–spectral hyperspectral image classification with sparse representation and markov random fields on GPUs. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2926–2938 (2015)
López-Fandiño, J., Heras, D.B., Argüello, F., et al.: GPU framework for change detection in multitemporal hyperspectral images. Int. J. Parallel Program. (2017). https://doi.org/10.1007/s10766-017-0547-5
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chidambaram, S., Sumathi, A. Optimal Feature Selection for the Classification of Hyperspectral Imagery Using Adaptive Spectral–Spatial Clustering. Int J Parallel Prog 48, 813–832 (2020). https://doi.org/10.1007/s10766-018-0607-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10766-018-0607-5