Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification

Recently, convolutional Neural Networks (CNNs) have exhibited commendable performance for hyperspectral image (HSI) classification. Generally, an important number of samples are needed for each class to properly train CNNs. However, existing HSI datasets suffer from a significant class imbalance problem, where many classes do not have enough samples to characterize the spectral information. The performance of existing CNN models is biased towards the majority classes, which possess more samples for the training. This paper addresses this issue of imbalanced data in HSI classification. In particular, a new 3D-HyperGAMO model is proposed which uses generative adversarial minority oversampling. The proposed 3D-HyperGAMO automatically generates more samples for minority classes at training time, using the existing samples of that class. The samples are generated in the form of a 3D hyperspectral patch. A different classifier from the generator and the discriminator is used in the 3D-HyperGAMO model, which is trained using both original and generated samples to {determine} the classes of newly generated samples to which they actually belong. The generated data are combined class-wise with the original training dataset to learn the network parameters of the class. Finally, the trained 3D classifier network validates the performance of the model using the test set. Four benchmark HSI datasets, namely Indian Pines~(IP), Kennedy Space Center~(KSC), University of Pavia~(UP), and Botswana~(BW), have been considered in our experiments. The proposed model shows outstanding data generation ability during the training, which significantly improves the classification performance over the considered datasets. The source code is available publicly at \url{https://github.com/mhaut/3D-HyperGAMO}.