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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...
Gabor features have been proved to be effective for the recently-proposed nearest regularized subspace (NRS) classifier. In this paper, we further investigate a residual fusion based strategy with multiple features and NRS. Multiple features include local binary patterns (LBP), Gabor features and the original spectral signatures. In the proposed classification framework, each type of feature is first...
Spectral–spatial preprocessing using multihypothesis prediction is proposed for improving accuracy of hyperspectral image classification. Specifically, multiple spatially collocated pixel vectors are used as a hypothesis set from which a prediction for each pixel vector of interest is generated. Additionally, a spectral-band-partitioning strategy based on inter-band correlation coefficients is proposed...
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