Multisource Domain Adaptation for Remote Sensing Using Deep Neural Networks

In applying machine learning to remote sensing problems, it is often the case that multiple training data sources, known as domains, are available for the same task. It is sample-inefficient to train separate models per domain, which motivates learning a single model from multiple sources. For example, the local climate zone (LCZ) classification problem that aims to produce per-pixel classifications of surface structure from remotely sensed images of urban and rural environments. These classification maps need to be generated for different cities at different times. To do this efficiently, available training data from different sources (i.e., cities) must be adapted for the task at hand. However, multisource domain adaptation (MDA) is a challenging problem and is particularly apparent when there are significant changes in the data distribution among these sources. In this article, we propose a scalable yet simple adaptive MDA (AMDA) framework to address this problem. AMDA is also capable of dealing with imbalanced data distributions among the sources more effectively than existing baselines. We also extend two techniques originally proposed for domain expansion (DE) to the task of DA. AMDA and the extended DE techniques are implemented and evaluated on the LCZ classification problem. Despite its simplicity, AMDA is able to achieve more than 12% improvement over the baseline.