Toward Optimum Fusion of Thermal Hyperspectral and Visible Images in Classification of Urban Area

Recently, classification of urban area based on multi-sensor fusion has been widely investigated. In this paper, the potential of using visible (VIS) and thermal infrared (TIR) hyperspectral images fusion for classification of urban area is evaluated. For this purpose, comprehensive spatial-spectral feature space is generated which includes vegetation index, differential morphological profile (DMP), attribute profile (AP), texture, geostatistical features, structural feature set (SFS) and local statistical descriptors from both datasets in addition to original datasets. Although Support Vector Machine (SVM) is an appropriate tool in the classification of high dimensional feature space, its performance is significantly affected by its parameters and feature space. Cuckoo search (CS) optimization algorithm with mixed binary-continuous coding is proposed for feature selection and SVM parameter determination simultaneously. Moreover, the significance of each selected feature category in the classification of a specific object is verified. Accuracy assessment on two subsets shows that stacking of VIS and TIR bands can improve the classification performance to 87 percent and 82 percent for two subsets, compare to VIS image (72 percent and 80 percent) and TIR image (50 percent and 56 percent). However, the optimum results obtained based on the proposed method which gains 94 percent and 92 percent. Furthermore, results show that using TIR beside VIS image improves classification accuracy of roads and buildings in urban area.