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Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields.

R. Nishii, Y. Sawamura, A. Nakamoto, and S. Kawaguchi. IGARSS (4), page 77-80. IEEE, (2008)

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Supervised image classification based on adaboost with contextual weak classifiers.R. Nishii, and S. Eguchi. IGARSS, page 1467-1470. IEEE, (2004)Analytical estimation of the convergence point of populations.N. Murata, R. Nishii, H. Takagi, and Y. Pei. CEC, page 2619-2624. IEEE, (2015)More precise tables of Srivastava-Chopra balanced optimal 2m fractional factorial designs of resolution V, m<=6R. Nishii, and T. Shirakura. Journal of Statistical Planning and Inference, (1986)Contextual image segmentation based on AdaBoost and Markov random fields.R. Nishii. IGARSS, page 3507-3509. IEEE, (2003)Enhancement of low spatial resolution image based on high resolution-bands.R. Nishii, S. Kusanobu, and S. Tanaka. IEEE Trans. Geosci. Remote. Sens., 34 (5): 1151-1158 (1996)Accuracy and inaccuracy assessments in land-cover classification.R. Nishii, and S. Tanaka. IEEE Trans. Geosci. Remote. Sens., 37 (1): 491-498 (1999)Non-linear regression models to identify functional forms of deforestation.S. Tanaka, and R. Nishii. IGARSS (4), page 49-52. IEEE, (2008)Contextual Image Classification Based on Spatial Boosting.R. Nishii. IGARSS, page 2137-2140. IEEE, (2006)Nonlinear Bayesian unmixing of geospatial data based on GIBBS sampling.R. Nishii, P. Qin, and D. Uchi. IGARSS, page 3104-3107. IEEE, (2013)The use of univariate Bayes regression models for spatial smoothingR. Nishii, T. Yanagimoto, and S. Kusanobu. Computational Statistics & Data Analysis, 24 (3): 321--336 (May 12, 1997)

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Disambiguation of "Nishii, Ryuei"

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Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields.

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Disambiguation of "Nishii, Ryuei"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields.

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Contextual Unmixing of Geospatial Data based on Gaussian Mixture Models and Markov Random Fields.