A fuzzy image segmentation approach for qualitative classification of land cover was proposed recently. In this letter, such an approach is applied for estimation of impervious surface areas from Landsat-TM images. The method involves four main stages: (i) pre-processing for radiometric normalization and independent component transformation, (ii) fuzzy segmentation to create fuzzy image regions representing membership values to land cover classes, (iii) feature analysis to evaluate contextual properties of fuzzy image regions, and (iv) regression to estimate impervious surface area. In this letter, a support vector machine technique was applied to conduct supervised learning tasks. Experimental results suggest that the method provides an accurate and simple alternative for quantitative analysis of urban land cover.