A cluster ensemble method for robust unsupervised classification of VHR remote sensing images

This paper present a novel ensemble method for clustering very high spatial resolution (VHR) images that is composed of four main steps. Firstly, because of the important role of the spatial component in VHR imagery, a set of morphological features are extracted from the original image using many openings and closings with increasing structural element sizes. Secondly, we construct the ensemble by running the k-means algorithm several times with different initializations. In order to increase the diversity, different subsets of features are randomly selected at each time. Third, an optimal relabeling of the ensemble with respect to a representative partition is made via a pairwise relabeling procedure. Finally, the relabeled maps are fused with a Markov Random Field (MRF) method. The Experimental results obtained on two real VHR images acquired by the sensors IKONOS-2 and GeoEye-1 over urban areas confirmed the promising capabilities of the proposed approach.