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Fusion of remote sensing data from multiple sensors has been remarkably increased for classification. This is because, additional sources may provide more information, and fusion of different information can produce a better understanding of the observed site. In the field of data fusion, fusion of light detection and ranging (LIDAR) and optical remote sensing data for land cover classification has attracted more attention. This paper addressed the use of a decision fusion methodology for the combination of hyperspectral and LIDAR data in land cover classification. The proposed method applied a support vector machine (SVM)-based classifier fusion system for fusion of hyperspectral and LIDAR data in the decision level. First, feature spaces are extracted on LIDAR and hyperspectral data. Then, SVM classifiers are applied on each feature data. After producing multiple of classifiers, Naive Bayes as a classifier fusion method combines the results of SVM classifiers form two data sets. A co-registered hyperspectral and LIDAR data set from Houston, USA, was available to examine the effect of the proposed decision fusion methodology. Experimental results show that the proposed data fusion method improved the classification accuracy and kappa coefficient in comparison to the single data sets. The results revealed that the overall accuracies of SVM classification on hyperspectral and LIDAR data separately are 88% and 58% while our decision fusion methodology receive the accuracy up to 91%.