Collaboration Between Multiple Experts for Knowledge Adaptation on Multiple Remote Sensing Sources
Ba Hung Ngo, Ju Hyun Kim, So Jeong Park, Sung In Cho
Abstract
Due to the unique characteristics of remote sensing (RS) data, it is challenging to collect richer labeled samples for training the deep learning model compared with the natural image data. To solve this problem, recently, multi-source-single-target (MS<sup>2</sup>T) scenarios have started receiving significant attention in which the knowledge from multiple sources is integrated to transfer to a target domain with the assumption that label spaces of each source and target domain are the same. However, in real-world applications, it can be challenging to find a source domain that completely includes all classes of the target domain. Therefore, to cover all class information of the target domain, they often naïvely merge multiple sources into a complete single source. However, each source domain typically has a different data distribution; thus, the semantic information of each source domain can be damaged, leading to degrading the classification accuracy of the target domain. To address this problem, we propose a unified framework termed Multi-Expert Collaboration for Knowledge Adaptation (MECKA) from various sources. MECKA includes two main processes: multiple-view generation and collaborative learning. Multiview learning plays an essential role in preserving the unique characteristics of each source domain. In contrast, collaborative learning is responsible for connecting these views that leverage complementary information from each other to perform on an unseen target domain robustly. Experimental results showed that the proposed method achieved the best classification accuracy on remote sensing scene benchmark datasets on both complete and incomplete multisource unsupervised domain adaptation (UDA) tasks compared to benchmark methods.