Source-Free Cross-Domain Scene Classification of Remote Sensing Images via Statistics Matching and Noise Adaptation

In recent years, in order to alleviate the performance degradation problem caused by domain drift in scene classification (SC) tasks, some unsupervised domain adaptation (UDA) methods have been introduced into the field of remote sensing images (RSIs). Such methods require simultaneous access to both source and target domain data during training. However, the large storage and transmission costs of RSIs limit the further application of these methods. To address this challenge, we investigate the task of source-free cross-domain SC (SFSC) for RSIs. In the model adaptation process, only the target domain dataset and the trained source domain model are used. Our approach consists of two parts: a distribution alignment strategy based on source domain model statistics matching and a noise adaptation strategy. In order to fully utilize the knowledge of the pretrained source domain model, we fix the classifier to get the feature distribution of the source domain, so that the target domain feature distribution is close to the feature distribution of the source domain. The noise adaptation layer is inserted after the classifier in order to improve the robustness of the model to noise-containing pseudo-labels, and the sample-wise noise transfer matrix is learned. Experimental results on 12 transfer tasks on the cross-scene dataset, and two transfer tasks on the cross-sensor dataset, to validate the effectiveness of our approach. Compared to traditional unsupervised domain adaptive methods, our method is able to achieve better performance under the condition of not accessing the source domain data.