Abstract
Hyperspectral image Contains rich information, so improving the classification ability of hyperspectral image has become a hot spot of research recently. Image segmentation is one of the most basic and important problems of the image processing and low level computer vision and the precondition of the image processing, some hyperspectral image classification method based on image segmentation. In this study, an image segmentation method based on kernel methods was proposed. First reduce the dimension of hyperspectral images by KPCA, then the k-means method are used to cluster, finally finish the image segmentation in the high-dimensional space by gaussian kernel mapping. In simulation experiment, changing the parameters of the experiment and comparing with standard hyperspectral image segmentation, the results show that the method is good enough in image segmentation, which can be used for visual analysis and pattern recognition, and realize hyperspectral image segmentation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bilgin, G., Erturk, S., Yildirim, T.: Segmentation of hyperspectral images via subtractive clustering and cluster validation using one-class support vector machines. IEEE Trans. Geosci. Remote Sens. 49(8), 2936–2944 (2011)
Erturk, A., Erturk, S.: Unsupervised segmentation of hyperspectral images using modified phase correlation. IEEE Geosci. Remote Sens. 3(4), 527–531 (2006)
Alajlan, N., Bazi, Y., Melgani, F., Yager, R.R.: Fusion of supervised and unsupervised learning for improved classification of hyperspectral images. Inf. Sci. 217, 39–55 (2012)
Muhammed, H.H.: Unsupervised hyperspectral image segmentation using a new class of neuro–fuzzy systems based on weighted incremental neural networks. In: IEEE 31st Applied Image Pattern Recognition Workshop, Washington, DC, pp. 171–177, October 2002
Silverman, J., Caefer, C.E., Mooney, J.M., Weeks, M.M.: An automated clustering/segmentation of hyperspectral images based on histogram thresholding. Proc. SPIE 4480, 65–75 (2002)
Silverman, J., Rotman, S.R., Caefer, C.E.: Segmentation of hyperspectral images based on histograms of principal components. Proc. SPIE 4816, 270–277 (2002)
Mercier, G., Derrode, S., Lennon, M.: Hyperspectral image segmentation with Markov chain model. In: IEEE Geoscience Remote Sensing Symposium, Toulouse, France, vol. 6, pp. 3766–3768, July 2003
Acito, N., Corsini, G., Diani, M.: An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model. In: IEEE IGARSS, Toulouse, France, vol. 6, pp. 3745–3747, July 2003
Farrell, M.D., Mersereau, R.: Robust automatic clustering of hyperspectral imagery using non-Gaussian mixtures. Proc. SPIE 5573, 161–172 (2004)
Shah, C.A., Watanachaturaporn, P., Arora, M.K., Varshney, P.K.: Some recent results on hyperspectral image classification. In: IEEE Advances Techniques Analysis Remotely Sensed Data, Greenbelt, MD, vol. 19, pp. 346–353 (2003)
Zhang, J., Chen, J., Zhang, Y., Zou, B.: Hyperspectral image segmentation method based on spatial-spectral constrained region active contour. In: IEEE Geoscience and Remote Sensing Symposium (IGARSS), pp. 2214–2217, July 2010
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn. 43(7), 2367–2379 (2010)
Tarabalka, Y., Tilton, J.C., Benediktsson, J.A., Chanussot, J.: A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations. IEEE Geosci. Remote Sens. 5(1), 262–272 (2012)
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Syst. Man Cybern. 40(5), 1267–1279 (2010)
Bernard, K., Tarabalka, Y., Angulo, J., Chanussot, J., Benediktsson, J.A.: Spectral–spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach. IEEE Image Process. 21(4), 2008–2021 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Lin, L., Du, J. (2018). Hyperspectral Image Segmentation Method Based on Kernel Method. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-319-63856-0_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-63856-0_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63855-3
Online ISBN: 978-3-319-63856-0
eBook Packages: EngineeringEngineering (R0)