Domain Adaptation for Satellite Images: Recent Advancements, Challenges, and Future Perspectives

Manel Khazri Khelif, Wadii Boulila, Anis Koubaa, Imed Riadh Farah

Abstract: Deep Learning (DL) has demonstrated remarkable success in various Remote Sensing Image (RSI) analysis applications. However, due to disparities in data distributions, DL models find it challenging to generalize meaningfully, especially when training and testing datasets are collected at different locations with varying resolutions, by different sensors, or due to climatic conditions. DL techniques applied to RSI have shown interest in domain adaptation as a suitable solution for addressing discordance among domains. In this study, we focus specifically on two DL approaches for Domain Adaptation (DA) in RSI: Self-Supervised Learning (SSL) and Graph Neural Networks (GNNs). First, we elucidate the motivation for utilizing DA techniques to address challenges in the field of RSI, along with their applications in conjunction with GNNs and SSL. Then, we present related surveys on domain adaptation and provide background information. This paper suggests a classification system for DL approaches and draws attention to challenges and research directions for DA in RSI. This study aims to deliver scholars in the remote sensing field with current references on DA using SSL and GNNs.