Mitigating Distribution Shift for Multi-Sensor Classification

Distribution shift may pose significant challenges in Earth observation, especially when dealing with significantly differ-ent sensors like multispectral optical and Synthetic Aperture Radar (SAR). Deep learning models trained for optical image classification generally do not generalize well for SAR images. This is due to very marked differences between them. Though there is a considerable amount of works on domain adaptation, only few deal with such strong differences. Towards this, we propose a co-teaching based domain adaptation method using dual classifier head, a Multi-layer Perceptron (MLP) classi-fier and a Graph Neural Network (GNN) classifier. The two classifier heads teach each other in an iterative manner, thus gradually adapting both of them for target classification. We experimentally demonstrate the efficacy of the proposed approach on Sentinel 2 (optical) as source and Sentinel 1 (SAR) images as target - both product of Copernicus program of European Space Agency.