Abstract
This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a Multi-Level Logistic Markov-Gibs prior. This density favors neighbouring labels of the same class. The adopted discriminative classifier is the Fast Sparse Multinomial Regression. The discrete optimization problem one is led to is solved efficiently via graph cut tools. The effectiveness of the proposed method is evaluated, with simulated and real AVIRIS images, in two directions: 1) to improve the classification performance and 2) to decrease the size of the training sets.
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References
Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43(6), 1351–1362 (2005)
Kumar, S., Hebert, M.: Discriminative Random Fields. International Journal of Computer Vision 68(2), 179–202 (2006)
Camps-Valls, G., Gomez-Chova, L., Muñoz-Marí, J., Vila-Francés, J., Calpe-Maravilla, J.: Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters 3(1), 93–97 (2006)
Plaza, A., Benediktsson, J., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Tilton, J., Trianni, G.: Advanced Processing of Hyperspectral Images. In: IEEE IGARSS Proceedings, vol. IV, pp. 1974–1979 (2006)
Borges, J.S., Bioucas-Dias, J., Marçal, A.R.S.: Fast Sparse Multinomial Regression Applied to Hyperspectral Data. In: Campilho, A., Kamel, M. (eds.) ICIAR 2006. LNCS, vol. 4142, pp. 700–709. Springer, Heidelberg (2006)
Krishnapuram, B., Carin, L., Figueiredo, M.A.T., Hartemink, A.J.: Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 957–968 (2005)
Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Hunter, D., Lange, K.: A Tutorial on MM algorithms. The American Statistician 58, 30–37 (2004)
Quarteroni, A., Sacco, R., Saleri, F.: Numerical Mathematics. TAM Series, vol. 37. Springer, Heidelberg (2000)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)
The MathWorks: MATLAB The Language of Technical Computing - Using MATLAB: version 6. The Math Works, Inc. (2000)
Landgrebe, D.A.: NW Indiana’s Indian Pine (1992), Available at http://dynamo.ecn.purdue.edu/~biehl/MultiSpec/
Boykov, Y., Kolmogorov, V.: An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)
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Borges, J.S., Bioucas-Dias, J.M., Marçal, A.R.S. (2007). Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_5
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DOI: https://doi.org/10.1007/978-3-540-72847-4_5
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