Clustering of Hyperspectral Images with an Ensemble Method Based on Fuzzy C-Means and Markov Random Fields

During the past few years, cluster ensemble methods have been introduced in the area of pattern recognition as a more accurate alternative to individual clustering algorithms as they can provide novel, robust, and stable solutions. The development of these methods is mainly motivated by the success of the combination of supervised classifiers also known as classifier ensemble. In this paper, we propose a novel ensemble method for the classification of hyperspectral images based on Fuzzy c-means (FCM) and Markov random field (MRF) theory. Firstly, in order to construct the ensemble of clustering maps, which call here “partitions”, we run the FCM algorithm several times with different initializations and different subsets of features (spectral bands) selected randomly from the high-dimensional input feature vector characterizing the hyperspectral image. Secondly, since there is no-ground truth information, the class labels contained in each generated partition are symbolic, meaning that the same class of pixels can be labeled with different labels in different partitions of the ensemble. Therefore, an optimal relabeling of the ensemble with respect to a representative partition determined on the basis of the maximum entropy principle is made via a pairwise relabeling procedure. Finally, in the last step, the relabeled partitions are fused by means of an MRF method. The latter exploits two kinds of sources of contextual information: spatial and inter-partition contextual information. During the optimization process, the contributions of the different partitions are controlled through mutual information weights.