Multispectral Band-Aware Generation of Satellite Images across Domains Using Generative Adversarial Networks and Contrastive Learning

Abstract

Generative models have recently gained popularity in remote sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and, especially, variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces an adaptive approach that harnesses the capabilities of generative adversarial networks (GANs), augmented with contrastive learning, to generate target domain images that account for multispectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multispectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multispectral diversity.