10 October 2017 Supervised classification of remotely sensed images using Bayesian network models and Kruskal algorithm
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Abstract
The aim of this work is to evaluate the performances of three Bayesian networks widely used for supervised image classification. The developed structures are constructed due to Kruskal algorithm which allows the determination of the maximum weight spanning tree by using the mutual information between the attributes. We started by the Bayesian naïve classifier (BNC), which assumes that there is no dependency, between the attributes to classify. In order to relax this strong assumption, we tested the tree augmented naïve Bayes classifier (TANC) where each feature has at most one variable as parent, and the forest augmented naïve Bayes classifier (FANC) where each attribute forms an arbitrary graph rather than just a tree. These classifiers are evaluated using a multispectral image and hyperspectral image in order to analyze the structure classifier complexity according to the number of attributes (04 and 10 spectral bands for the two images respectively). Obtained results are compared with state-of-the art competitor, namely, the SVM classifier. Classified images by TANC and FANC achieved higher accuracies than other classifiers including SVM. It is concluded that the choice of attributes dependencies significantly contributes to the discrimination of subjects on the ground. Thus, Bayesian networks appear as powerful tool for multispectral and hyperspectral image classification.
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Radja Kheddam, Youcef Boudissa, Aichouche Belhadj-Aissa, "Supervised classification of remotely sensed images using Bayesian network models and Kruskal algorithm", Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271K (10 October 2017); doi: 10.1117/12.2278122; https://doi.org/10.1117/12.2278122
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