Method for classifying a photographic satellite ground image for distinguishing features in said image, such as roads, housing, forest, arable land and parks, characterised in that the method comprises the steps that a computer or several interconnected computers are caused to a) store, in the form of a pixel set in which set each pixel is associated with image information in at least one channel for light intensity, a first image to be classified onto a digital storage medium; b) carry out a first classification of the image, which classification comprises a first K-means classification based upon the image information of each respective pixel and which classification is caused to associate each pixel with a certain class in a first set of classes, obtained as the clusters of the first K-means cluster analysis, and to store these associations in a first database; c) calculate, for each pixel and for all classes in the first set of classes, the smallest distance in the image between the pixel in question and the closest pixel which is associated with the class in question in the database, and to store an association between each pixel and the calculated smallest distance for the pixel in a second database for each class for which a distance has been calculated; d) carry out an unsupervised second classification of the data in the second database, which classification comprises a second K-means cluster analysis using the smallest respective distance for each pixel to each respective one of said first classes as coordinates in a multi-dimensional feature space, and to associate each pixel to a certain class in a second set of classes using only said calculated smallest distances, which second set of classes is obtained as the clusters of the second K-means cluster analysis; and e) store the classified image in the form of a set of pixels onto a digital storage medium, where each pixel comprises data regarding the association of the pixel to the certain class in the second set of classes, and where the classified image has the same dimensions as the first image. a) store, in the form of a pixel set in which set each pixel is associated with image information in at least one channel for light intensity, a first image to be classified onto a digital storage medium; b) carry out a first classification of the image, which classification comprises a first K-means classification based upon the image information of each respective pixel and which classification is caused to associate each pixel with a certain class in a first set of classes, obtained as the clusters of the first K-means cluster analysis, and to store these associations in a first database; c) calculate, for each pixel and for all classes in the first set of classes, the smallest distance in the image between the pixel in question and the closest pixel which is associated with the class in question in the database, and to store an association between each pixel and the calculated smallest distance for the pixel in a second database for each class for which a distance has been calculated; d) carry out an unsupervised second classification of the data in the second database, which classification comprises a second K-means cluster analysis using the smallest respective distance for each pixel to each respective one of said first classes as coordinates in a multi-dimensional feature space, and to associate each pixel to a certain class in a second set of classes using only said calculated smallest distances, which second set of classes is obtained as the clusters of the second K-means cluster analysis; and e) store the classified image in the form of a set of pixels onto a digital storage medium, where each pixel comprises data regarding the association of the pixel to the certain class in the second set of classes, and where the classified image has the same dimensions as the first image.