A hierarchical naive Bayesian network classifier embedded GMM for textural image

Abstract To address several problems in high-dimensional textures feature space and the deficiencies of the single Gaussian distribution for remote sensing data, this paper proposes a hierarchical naive Bayesian network classifier embedded in a Gaussian mixture model for high-dimensional textural image classification. High-dimensional features are grouped by the model on the basis of the correlations between them. In this way, the high-dimensional problem is decomposed into multiple problems of lower dimension. At the same time, for each group of features, a Gaussian mixture model is applied to simulate the data distribution in feature space for land covers, which fits the “original” data distribution better than a single Gaussian model. The Gaussian mixture model is embedded as a child node into a naive Bayesian network, and then the final classification result is obtained within the naive Bayesian network classifier framework. Experimental results for the classification of Landsat ETM+ and QuickBird image textures demonstrated that the classification accuracy of this method is better than that of a traditional Bayesian network classifier and some other classical classifiers. Comparing with the method dealing with original high-dimensional features, it is also more efficiency and effectiveness with fewer demand of sample size and lower time complexity.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Cristina Solares,et al.  Different Bayesian Network Models in the Classification of Remote Sensing Images , 2007, IDEAL.

[3]  Thomas D. Nielsen,et al.  Classification using Hierarchical Naïve Bayes models , 2006, Machine Learning.

[4]  William J. Emery,et al.  A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification , 2009 .

[5]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[6]  Liliane dos Santos Machado,et al.  Gaussian Mixture Models for Supervised Classification of Remote Sensing Multispectral Images , 2003, CIARP.

[7]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[8]  Liming Zhang,et al.  Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model , 2009, Pattern Recognit. Lett..

[9]  Aly A. Farag,et al.  Advanced algorithms for Bayesian classification in high dimensional spaces with applications in hyperspectral image segmentation , 2005, IEEE International Conference on Image Processing 2005.

[10]  Zoë Hoare,et al.  Landscapes of Naïve Bayes classifiers , 2007, Pattern Analysis and Applications.

[11]  Eric D. Kolaczyk,et al.  Gaussian mixture discriminant analysis and sub-pixel land cover characterization in remote sensing , 2003 .

[12]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[13]  Jonathan M. Garibaldi,et al.  A 'non-parametric' version of the naive Bayes classifier , 2011, Knowl. Based Syst..

[14]  Gustavo Camps-Valls,et al.  Learning Relevant Image Features With Multiple-Kernel Classification , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Luis O. Jimenez-Rodriguez,et al.  Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Anil K. Jain,et al.  Unsupervised Learning of Finite Mixture Models , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Tae Jin Kang,et al.  Texture classification and segmentation using wavelet packet frame and Gaussian mixture model , 2007, Pattern Recognit..

[18]  David A. Landgrebe,et al.  Hyperspectral image data analysis , 2002, IEEE Signal Process. Mag..

[19]  Haim H. Permuter,et al.  A study of Gaussian mixture models of color and texture features for image classification and segmentation , 2006, Pattern Recognit..

[20]  William J. Emery,et al.  Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Yang Li,et al.  Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning , 2007, IDEAL.

[22]  Wei Su,et al.  Textural and local spatial statistics for the object‐oriented classification of urban areas using high resolution imagery , 2008 .

[23]  Toon Calders,et al.  Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.

[24]  Hong Yan,et al.  An Effective Method for Classification of High Dimensional Data , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[25]  Peng Gong,et al.  Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis , 2003 .

[26]  Ronald R. Yager,et al.  An extension of the naive Bayesian classifier , 2006, Inf. Sci..

[27]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[28]  Xavier Descombes,et al.  An unsupervised clustering method using the entropy minimization , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).