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The problem of classification of high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest in exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile obtained with a granulometric approach using respectively opening and closing operators. We propose to replace this by a morphological alternated sequential filter, where the openings and the closings are applied alternately. The results and the robustness provided by the ASF are presented on IKONOS panchromatic data.