Representation-based hyperspectral image classification with imbalanced data


This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. We exploit an orthogonal projection based algorithm to select samples from large-size ones; meanwhile, we propose an algorithm based… (More)
DOI: 10.1109/IGARSS.2016.7729858


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