SIGAN: Spectral Index Generative Adversarial Network for Data Augmentation in Multispectral Remote Sensing Images

Generative models are typically employed to approximate the distribution of deep features. Recently, these state-of-the-art methods have been applied to estimate image transformations by an unsupervised learning approach. In this letter, a novel spectral index generative adversarial network (SIGAN) is proposed for the generation of multispectral (MS) remote sensing images. This network is defined to effectively perform data augmentation starting from a limited number of training samples in the MS remote sensing domain for training deep learning models. The SIGAN model is able to capture class-specific properties in data augmentation, by incorporating the task-specific normalized spectral indices to model class-by-class properties of MS images. Experimental results obtained on a Sentinel 2 dataset show that the proposed model provides better performance than other generative adversarial networks (GANs) in MS data generation.
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