Source-Free Domain Adaptation for Cross-Scene Hyperspectral Image Classification

Abstract

Deep learning based cross-domain hyperspectral image (HSI) classification methods were proposed to train a classifier adapted to unlabeled target domain with the help of abundant labeled data in source domain. Although the existing methods show their potential for cross-domain HSI classification, the data in source domain may not be provided due to the data privacy, which limits the availability of these methods. In this case, how to utilize the model or knowledge trained from source domain becomes a more challenging problem. In this study, we emphasize on this problem, and propose source-free unsupervised domain adaptation method for HSI classification. Specifically, we firstly design a source domain HSI spectral feature generator, and then realize the class-wised alignment between the generated source domain HSI spectral features and the target domain features of HSI through contrastive learning. To solve the dilemma of without labels in the target domain, we also utilize a logits-weighted prototype classifier to iteratively obtain the data label of the target domain. Experiments on two cross-scene HSI datasets demonstrate the effectiveness of the proposed method when only providing the model trained from the source domain.