Abstract
This paper presents a novel nonparametric supervised spectral-spatial classification method for multispectral image. In multispectral images, if an unknown pixel shows similar digital number (DN) vectors as pixels in the training class, it will obtain higher posterior probability when assuming DN vectors of different classes follow a type of statistical distribution. The proposed method assumes the DN vectors follow a Gaussian mixture distribution in each class. Particularly, we use Bayesian nonparametric method to adaptively estimate the parameters in Gaussian mixture model. Then, we construct an anisotropic multilevel logistic spatial prior to capture the spatial contextual information provided by multispectral image. Finally, simulated annealing optimization algorithm is used to accomplish the maximum a posteriori classification. The proposed approach is compared with recently advanced multispectral image classification methods. The comparison results of classification suggested that the proposed approach outperformed other classifiers in overall accuracy and kappa coefficient.
Chapter PDF
Similar content being viewed by others
Keywords
References
Foody, G.M.: Status of land cover classification accuracy assessment. Remote Sens. Environ. 80(1), 185–201 (2002)
Townsend, P.A., Helmers, D.P., Kingdon, C.C., McNeil, B.E., de Beurs, K.M., Eshleman, K.N.: Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976–2006 Landsat time series. Remote Sens. Environ. 113(1), 62–72 (2009)
Shackelford, A.K., Davis, C.H.: A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 41(9), 1920–1932 (2003)
Insanic, E., Siqueira, P.R.: A maximum likelihood approach to estimation of vector velocity in Doppler radar networks. IEEE Trans. Geosci. Remote Sens. 50(2), 553–567 (2012)
Ruiz, P., Mateos, J., Camps-Valls, G.: Bayesian active remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 52(4), 2186–2196 (2014)
Gershman, S.J., Blei, D.M.: A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology 56(1), 1–12 (2012)
Fauvel, M., Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Tilton, J.C.: Advances in spectral–spatial classification of hyperspectral images. Proc. IEEE 101(3), 652–675 (2013)
Li, J., Bioucas-Dias, J.M., Plaza, A.: Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields. IEEE Trans. Geosci. Remote Sens. 50(3), 809–823 (2012)
Moser, G., Serpico, S.B.: Combining support vector machines and Markov random fields in an integrated framework for contextual image classification. IEEE Trans. Geosci. Remote Sens. 51(5), 2734–2752 (2013)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2007)
da Silva, A.R.F.: A Dirichlet process mixture model for brain MRI tissue classification. Med. Image Anal. 2, 169–182 (2007)
Nguyen, N.T., Zheng, R., Han, Z.: On identifying primary user emulation attacks in cognitive radio systems using nonparametric bayesian classification. IEEE Transactions on Signal Processing 60(3), 1432–1445 (2012)
Neal, R.M.: Markov chain sampling methods for dirichlet process mixture models. Journal of Computational and Graphical Statistics 9(2), 249–265 (2000)
Wood, F., Goldwater, S., Black, M.J.: A nonparametric bayesian approach to spike sorting. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1165–1168 (2006)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 10(6), 721–741 (1984)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, S., Wang, Y., Li, J., Gao, X. (2015). Multispectral Image Classification Using a New Bayesian Approach with Weighted Markov Random Fields. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-662-48558-3_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48557-6
Online ISBN: 978-3-662-48558-3
eBook Packages: Computer ScienceComputer Science (R0)