A Weakly Pseudo-Supervised Decorrelated Subdomain Adaptation Framework for Cross-Domain Land-Use Classification

High spatial resolution (HSR) remote-sensing image scene classification is a crucial way for land-use interpretation. However, most of the current scene classification methods assume that the training and test sets of remote-sensing images follow the same feature distribution. In practical application, this assumption is difficult to guarantee. Domain adaptation (DA) is a machine-learning paradigm that can effectively alleviate such problems. However, previous studies mostly focused on aligning the global distribution of the source domain (SD) and the target domain (TD), which loses the inter-subdomain contextual relations between both domains and ignores the redundancy among features. However, most DA methods usually only use the manually designed measurement criteria to establish the relationship between the SD and the TD, which is insufficient or complicated. In this article, a weakly pseudo-supervised decorrelated subdomain adaptation (WPS-DSA) framework is proposed for HSR cross-domain land-use classification. In WPS-DSA, a feature extractor based on the subdomain adaptation network is used to extract the inter-subdomain characteristics of both domains. To weaken the influence of the features redundancy among remote-sensing images, the switchable whitening (SW) module is introduced. In addition, a domain hierarchical sampling (DHS) mechanism is designed to strengthen the connection between SD and the TD simply. Moreover, the WuHan-ShangHai (WH-SH) dataset that is sampled from two typical Chinese cities is constructed to verify the generalization of the proposed framework. The experimental results of the cross-domain tasks on three publicly available HSR datasets and the WH-SH DA dataset display considerable performance and generalization ability of WPS-DSA.