With the multiplication of satellite images with complementary spatial and spectral resolution, a major issue in the classification process is the simultaneous use of several images. In this context, the objective of this letter is to propose a new method which uses information contained in both spatial resolution. The main idea is that on one hand, the semantics level associated with an image depends on its spatial resolution, and on the other hand, information given by these images are complementary. The goal of this multiresolution image method is to automatically build a classification using knowledge extracted from both images, by unsupervised way and without pre-processing image fusion. The method is tested by using a Quickbird (2.8m) and a SPOT-4 (20m)images on the urban area of Strasbourg (France). The experiments have shown that the results are better than a classical unsupervised classification on each image, and comparable to a supervised region-based classification on the HSR image. Index Terms-multiresolution, clustering, remote sensing image analysis, high spatial resolution. This is the author's version of an article published in IEEE Geoscience and Remote Sensing Letters. The final authenticated version is available online at: http://dx.