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Hyperspectral image classification based on improved few shot learning

Aiming at the problem that hyperspectral image labeling training samples are less likely to be over-fitting, and the network cannot be well constrained during training, this paper adopts few shot learning classification method of reconstruction loss function. This method solves the problems of feature difference within a class and distance between features by constructing a loss function. The algorithm is applied to PaviaU, Ksc and Salinas hyperspectral images. The experimental results show a certain effect, and a better performance improvement has been achieved in the image classification task.