Representation-based hyperspectral image classification with imbalanced data

Jiaojiao Li, Qian Du, Wei Li, Yunsong Li

Abstract: 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 on the orthogonal complementary subspace projection to create artificial samples for small-size ones. The impact on representation based classifiers, i.e., sparse representation based classifier and collaborative representation based classifier, are investigated. Experimental results demonstrate that it can outperform other traditional solutions.