SIGAN: Spectral Index Generative Adversarial Network for Data Augmentation in Multispectral Remote Sensing Images
Abhishek Singh, Lorenzo Bruzzone
Abstract
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.