We present a new hierarchical image segmentation algorithm, which extends a fast multiscale iterated weighted aggregation method originally developed by Brandt et al.(1, 2) to multichannel data. This approach, combining both a region-based and an edge-based approach, results well suited for data (like remote sensing images), which present heterogeneous multiscale characteristics. The generalization of the weighted aggregation method to multispectral remote sensing data requires the choice of a suitable statistical model to describe the multichannel data, together with an appropriate spectral homogeneity criterion. To this purpose, we adopted the multivariate Gaussian model, and the Bhattacharyya distance. For multivariate Gaussian distribution the Bhattacharyya distance results in a closed expression, which is an analytical function of the mean and covariance matrix. Such statistical moments can be computed recursively during the segmentation process, preserving the overall linear complexity of the segmentation process. Our segmentation method has been successfully integrated into Knowledge Information Mining (KIM), a tool developed within an ESA research project for content-based image retrieval from remote sensing large archives. Moreover, the proposed segmentation procedure has been extensively used within the DesertWatch project, an ESA Data User Program (DUP) project for the study and the monitoring of desertification processes from multiple Earth Observation data.