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The ultimate goal of pattern recognition systems in remote sensing is to achieve the best possible classification performance for recognition of different objects such as buildings, roads and trees. From a scientific perspective, the extraction of roads in complex environments is one of the challenging issues in photogrammetry and computer vision, since many tasks related to automatic scene interpretation are involved. Roads have homogeneous reflectivity in LIDAR intensity and the same height as bare surface in elevation. Proposed method in this paper is based on combining multiple classifiers (MCS) is one of the most important topics in pattern recognition to achieve higher accuracy. Majority Voting and Selective Naïve Bays are two methods that used for fusion of classifiers.I.

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With recent technological advances in remote sensing sensors and systems, very highdimensional hyperspectral data are available for a better discrimination among different complex landcover classes. However, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon or 'curse of dimensionality' in hyperspectral data sets. Moreover, these high numbers of bands are usually highly correlated. Because of these complexities of hyperspectral data, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifier in these situations, Multiple Classifier Systems (MCS) may have better performance than single classifier. This paper presents a new method for classification of hyperspectral data based on a band clustering strategy through a multiple Support Vector Machine system. The proposed method uses the band grouping process based on a modified mutual information strategy to split data into few band groups. After the band grouping step, the proposed algorithm aims at benefiting from the capabilities of SVM as classification method. So, the proposed approach applies SVM on each band group that is produced in a previous step. Finally, Naive Bayes (NB) as a classifier fusion method combines decisions of SVM classifiers. Experimental results on two common hyperspectral data sets show that the proposed method improves the classification accuracy in comparison with the standard SVM on entire bands of data and feature selection methods.