In this paper, the performance of different texture measures for detection of urban areas from SAR data is evaluated. The used texture measures are categorized into two groups, the first group include the SAR specific textures and the second one considers the general texture measures. ffmax is selected from the first category and LISA, SRPD, Wavelet measures and fractal dimensions are used as general texture measures. For a better discrimination, all texture measures are calculated and a PCA rotation is applied to them and the first PC is multiplied by the urban inhomogeneity parameter and the obtained image is segmented. The obtained results of this procedure comparing with the K-Means clustering algorithm show the better performance of this algorithm for urban area detection.