SPIE Journal Paper | 19 July 2018
KEYWORDS: Image classification, Vegetation, Remote sensing, Satellite imaging, Satellites, Multispectral imaging, Earth observing sensors, Data modeling, Image processing, Optical inspection
Bayesian network classifiers (BNCs) are now among the most used supervised probabilistic methods for remote sensing image classification. Our contribution lies in two principal points. First, the investigation of the applicability of Kruskal’s algorithm constructs the optimal tree structure of multinet Bayeisan network classifier (MBNC). Second, the focus on MBNC’s advantages is over other classical BNCs, such as naive Bayes classifier (NBC), tree augmented naive Bayes classifier (TANC), forest augmented naive Bayes classifier (FANC), and state-of-the art competitor classifiers such as maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. While classical BNCs have a mere network for all predefined classes, MBNC has as many local Bayesian networks as the predefined classes. Hence, through a statistical evaluation and a visual inspection, our objective is to emphasize the contribution of MBNC to enhance the accuracy of urban land cover map obtained by classification of remotely sensed image. Performances of developed BNCs (NBC, TANC, FANC, and MBNC) are experimentally assessed using a multispectral image acquired on July 12, 2010, by Alsat-2A Algerian satellite. Based on a confusion matrix, overall accuracy, and Kappa statistic, results indicate that MBNC largely outperforms classical BNCs (NBC, TANC, and FANC) and probabilistic MLC, but performs slightly better than an SVM classifier. Due to its specific-class local network, MBNC gives a powerful tool for a better discrimination between different correlated spectral classes.
