Data Augmentation Through Spectrally Controlled Adversarial Networks for Classification of Multispectral Remote Sensing Images

Availability of limited training remote sensing datasets is one of the problems in deep learning, as deep architectures require a large number of training samples for proper training. In this paper, we present a technique for data augmentation based on a spectral indexed generative adversarial network to train deep convolutional neural networks. This technique uses the spectral characteristic of multispectral (MS) images to support data augmentation in order to generate realistic training samples with respect to each land-use and land-cover class. The impact of multispectral remote sensing data generated through the spectral indexed GAN are evaluated through classification experiments. Experimental results obtained on the classification of the Sentinel-2 Eurosatallband datasets show that data augmentation through spectral indexed GAN enhances the main accuracy metrics.