Abstract
Traditional hyperspectral image classification methods always focused on spectral information, and lots of spatial information was neglected. Therefore, this paper introduces the spatial texture information in the process of hyperspectral image classification, and focuses on how to deeply combine the texture information and the spectral information. Based on empirical mode decomposition and local binary pattern, the method of support vector machine is used to classify hyperspectral image, in order to improve the image classification accuracy.
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
Tian, Y.-P., Tao, C., Zou, Z.-G.: Hyperspectral image classification based on semi-supervision of active learning and graph. Acta Geodaetica Cartogr. Sin. 44(8), 919–926 (2015)
Jin, J., Zou, Z.-R., Tao, C.: High-resolution remote sensing image compression texture element classification. Acta Geodaetica Cartogr. Sin. 43(5), 493–499 (2014)
Zhang, W., Du, P.-J., Zhang, H.-P.: Study on hyperspectral mixed pixel decomposition method based on neural network. Bull. Surv. Mapp. 7, 23–26 (2007)
Pei-jun, D., Lin, H., Sun, D.-x.: Progress of hyperspectral remote sensing classification based on support vector machine. Bull. Surv. Mapp. 12, 37–40 (2006)
Wenying, H., Jiao, Y.-m.: Remote sensing image texture information extraction method. Yunnan Geogr. Environ. Res. 3(19), 17–20 (2007)
Ojala, T., Harwood, I.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Masood, K., Rajpoot, N.: Texture based classification of hyperspectral colon biopsy samples using CLBP. In: IEEE International Symposium on Biomedical Imaging, Boston, MA, USA, pp. 1011–1014, 01 July 2009
Huang, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. In: Proceeding of Royal Society, London, vol. A454, pp. 903–995 (1998)
Mäenpää, T.: The Local Binary Pattern Approach to Texture Analysis-Extension and Application [EB/OL] (2006). http://herkules.oulu.fi/isbn9514270762/
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grant No. 61563036 and the Fundamental Research Funds for the Central Universities in China under Grant No. 2013B32514.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, C., Zuo, H., Wang, X., Shi, A., Fan, T. (2017). Hyperspectral Image Classification Based on Empirical Mode Decomposition and Local Binary Pattern. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-67777-4_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67776-7
Online ISBN: 978-3-319-67777-4
eBook Packages: Computer ScienceComputer Science (R0)