Band aware domain generalization for cross-country multispectral remote sensing scene classification

Domain shift refers to the overall distribution differences between data used for model development and post-deployment. If not addressed, it can typically lead to performance degradation in operational settings. It is especially emphasized in the context of remote sensing, where scenes commonly capture large areas with significant geographical, temporal, and sensor variations. Domain generalization is a type of transfer learning for addressing this issue, often through feature alignment, that on the contrary of domain adaptation, assumes access to neither target domain labels nor to target domain data. In this study, we explore the progressive alignment of an image's spectral bands, instead of handling them collectively and concurrently. Experiments have been conducted with Sentinel-2 multi-spectral images, with six European countries denoting the domains, using various contemporary domain generalization techniques, and it is shown that a gradual alignment of spectral bands leads to consistent performance improvements.