FAULT TOLERANT UNSUPERVISED KERNEL-BASED INFORMATION CLUSTERING IN HYPERSPECTRAL IMAGES

In this work we derive a novel clustering scheme for hyper spectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation we employ gradient descent iterations that enable a computationally efficient determination of proper sparse clustering matrices. Extensive numerical tests on real hyperspectral images reveal that the proposed approach, in spite of being unsupervised, can outperform existing supervised and unsupervised techniques especially in the presence of missing pixels that may be caused by malfunctioning in the data acquisition system.