Statistical Fusion-Based Transfer Learning for Hyperspectral Image Classification

Hyperspectral images (HSIs) has practical applications in many fields. In practical scenarios, machine learning often fails to handle changes between training (source) and testing (target) input distributions due to domain shifts. A big challenge in hyperspectral image classification is the small size of labeled data for classifier learning. We always face the situation that an HSI scene is not labeled all or with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with similar land cover classes. In this paper, we propose a simple and effective method for domain adaptation called statistical fusion to minimize domain shifts by aligning the second-order and fourth-order statistics of source and target distributions. After two hyperspectral scenes are transformed into the similar property-space, any traditional HSI classification approaches can be used, and experimental results have validated the generalization of the proposed method.