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Conventional multispectral classification methods show poor performance in the detection of urban object, due to the high within-class spectral variance of classes corresponding to complex urban areas. In this paper, to improve the classification accuracy, we propose a data fusion approach based on the joint use of spectral and spatial information provided by the texture features extracted from the Gray Level Co-occurrence Matrix (GLCM). Specifically, a three-stage process characterizes our approach. The first stage concerns texture feature extraction considering several combinations of the three GLCM parameters: window size, step and angle. In the second stage a feature selection algorithm is applied to reduce the redundancy of the feature vector composed of both spectral and texture features. The third stage is a supervised classification. Finally, we propose an adaptive approach to extract the GLCM features which exploits the spatial information provided by a conventional segmentation algorithm. The proposed approach has been tested by using IKONOS data at 4 m resolution.