The present paper deals with the land cover classification of high resolution Quickbird images using the texture feature analysis. The study area covers the wider region of the urbanized environment of Chania, Greece. Different textural features including Entropy and Asm (angular second moment) were extracted based on GLCM (Grey Level Co-occurrence Matrix) texture feature and used as the distinct feature value in classification procedures. The classification was performed on the texture image that was produced by the synthesis of the original image with vegetation index BRI (band ratio index) extracted from the original datasets. Results indicate that the proposed approach brings significant improvement of the classification rate based on the different texture feature images of various bands, allowing a better discrimination and mapping of mixed land cover types.