Adversarial discriminative knowledge transfer with a multi-class discriminator for robust geoai

In geospatial image analysis applications, many factors can result in statistical differences between training and testing/deployment conditions. Domain adaptation techniques aim to reduce these disparsities with the goal of improving image analysis performance. Despite recent progress, some challenges remain, such as insufficient discriminability in the aligned space and negative transfer. We introduce a novel semi-supervised domain adaptation approach that improves the adversarial discriminative domain adaptation framework, addressing these challenges. We validate its effectiveness using two real-world hyperspectral image analysis datasets with varying acquisition conditions.