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We propose here a new algorithm for the unsupervised segmentation of multiresolution remote-sensing images. After a first segmentation step on the high-resolution panchromatic data, the image is converted in a set of disjoint regions, which are then clustered and merged progressively, based on multispectral, spatial and textural properties, producing a sequence of nested segmentation maps which provide a thorough and multi-scale description of the image. The algorithm is fast, since it works mainly at a region level, and preserves fine details thanks to the initial step at the high-resolution level. Experimental results on IKONOS data confirm the algorithm potential and point out to a few problems to address in future research.