Classification of remote sensing images from urban areas using a fuzzy model
The problem of classification of high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest of exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile (DMP) obtained with a granulometric approach, using respectively opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this paper, this vector is considered as a fuzzy measurement of the size of the structure. Compared with some possibility distributions, a membership degree is computed for each class. The decision is taken by selecting the class with the highest membership degree.