Abstract
Hyperspectral images are a series of images, which are captured for a specific region over a range of wavelengths. This makes the classification process computationally more expensive. For reducing the computational complexity, instead of considering all bands, it is essential to select the most informative bands. In this paper, a probabilistic histogram-based band selection approach is proposed. Here, adjacent band fusion with a class-specific deviation is computed followed by extraction of fused band intra- and inter-class histogram properties, to rank the bands with ensemble probability. In both the steps, median measure is used to half the total dimension. So finally, one-fourth of the optimal bands are obtained. Both spectral and spatial features of the reduced bands are considered for classification using KNN with different distance measures. Performance measures like accuracy and execution time are compared. Even by considering only 5% of optimal bands, the proposed approach maintains reference accuracy with reduced computational complexity.
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
Dobigeon, N., Tourneret, J.Y., Chang, C.I.: Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery. IEEE Trans. Sig. Process. 56(7), 2684–2695 (2008)
Du, Q., Yang, H.: Unsupervised band selection for hyperspectral image analysis. Int. Geosci. Remote Sens. Symp. (IGARSS) 5(4), 282–285 (2007)
Feng, F., Li, W., Du, Q., Zhang, B.: Dimensionality reduction of hyperspectral image with graph-based discriminant analysis considering spectral similarity. Remote Sens. 9(4), 323 (2017)
Franchi, G., Angulo, J.: Morphological principal component analysis for hyperspectral image analysis. ISPRS Int. J. Geo-Inf. 5(6), 83 (2016)
Huang, K., Li, S., Kang, X., Fang, L.: Spectral-spatial hyperspectral image classification based on knn. Sens. Imaging 17(1), 1 (2016)
Ifarraguerri, A., Prairie, M.W.: Visual method for spectral band selection. IEEE Geosci. Remote Sens. Lett. 1(2), 101–106 (2004)
Jia, S., Ji, Z., Qian, Y., Shen, L.: Unsupervised band selection for hyperspectral imagery classification without manual band removal. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5(2), 531–543 (2012)
Kun, T., Erzhu, L., Qian, D., Peijun, D.: Hyperspectral image classification using band selection and morphological profiles. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7(1), 40–48 (2014)
Laparra, V., Malo, J., Camps-Valls, G.: Dimensionality reduction via regression in hyperspectral imagery. IEEE J. Sel. Top. Sig. Process. 9(6), 1026–1036 (2015)
Li, S., Wu, H., Wan, D., Zhu, J.: An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine. Knowl.-Based Syst. 24(1), 40–48 (2011)
Li, S., Qiu, J., Yang, X., Liu, H., Wan, D., Zhu, Y.: A novel approach to hyperspectral band selection based on spectral shape similarity analysis and fast branch and bound search. Eng. Appl. Artif. Intell. 27, 241–250 (2014)
Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113(SUPPL. 1), S110–S122 (2009)
Sarhrouni. E., Hammouch, A., Aboutajdine, D.: Band selection and classification of hyperspectral images by minimizing normalized mutual information. In: Second International Conference on the Innovative Computing Technology (INTECH 2012), pp. 184–189 (2012)
Serpico, S.B., Bruzzone, L.: A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans. Geosci. Remote Sens. 39(7), 1360–1367 (2001)
Yang, H., Du, Q., Chen, G.: Unsupervised hyperspectral band selection using graphics processing units. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4(3), 660–668 (2011)
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, H., Du, Q., Chen, G.: Particle swarm optimization-based hyperspectral dimensionality reduction for urban land cover classification. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5(2), 544–554 (2012)
Zhang, X., Sun, Q., Li, J.: Optimal band selection for high dimensional remote sensing data using genetic algorithm. In: Second International Conference on Earth Observation for Global Changes, vol. 7471, pp. 74711R–74711R–7 (2009)
Zortea, M., Plaza, A.: Spatial preprocessing for endmember extraction. IEEE Trans. Geosci. Remote Sens. 47(8), 2679–2693 (2009)
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
Patro, R.N., Subudhi, S., Biswal, P.K., Sahoo, H.K. (2019). Probabilistic Histogram-Based Band Selection and Its Effect on Classification of Hyperspectral Images. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_44
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)