Generating labeled samples for hyperspectral image classification using correlation of spectral bands

Because the labor needed to manually label a huge training sample set is usually not available, the problem of hyperspectral image classification often suffers from a lack of labeled training samples. At the same time, hyperspectral data represented in a large number of bands are usually highly correlated. In this paper, to overcome the small sample problem in hyperspectral image classification, correlation of spectral bands is fully utilized to generate multiple new sub-samples from each original sample. The number of labeled training samples is thus increased several times. Experiment results demonstrate that the proposed method has an obvious advantage when the number of labeled samples is small.

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