CNN Hyperspectral Image Classification Using Training Sample Augmentation with Generative Adversarial Networks

Abstract

A big challenge for hyperspectral image recognition is to perform pixel classification when only a few hyperspectral training labeled pixels are available. In this research we have built Generative Adversarial Networks (GANs) that generate additional virtual training hyperspectral pixels based on features extracted from the originally labeled pixels belonging to training dataset. The experiments show a better performance of pixel classification for a classifier based on Deep Convolutional Neural Networks (DCNNs) using GANs for training sample augmentation versus the performance of the DCNN classifier without GAN augmentation. The score of 95.32% correct classification using DCNN classifier with GANs versus the score of 92.94% of DCNN classifier without GANs proves the obvious advantage of the presented approach.