Deep Geospatio-Semantic Guided Network With Pseudo-Label Consistency for Domain-Adaptive Remote Sensing Segmentation

Domain-adaptive Remote Sensing Images (RSIs) semantic segmentation mitigates the overfitting problem that affects the effectiveness of segmentation, which results from the scarcity of high-quality labels and the cross-domain styles of ground objects. The effectiveness of domain adaptive segmentation remains suboptimal in complex scenarios due to inadequate exploitation of latent geographic knowledge. Consequently, inter-class ambiguity and boundary agnostic are further exacerbated under cross-domain transfer scenarios. To address this issue, we first devise a deep geospatio-semantic guided network named DSSAL, which comprehensively investigates the potential spatial relationship and semantic correlation between classes of RSIs by geospatial aware interaction and geosemantic aware interaction, respectively. To mitigate class-wise cognitive deviation in the unlabeled domain, DSSAL-DA is developed to further enhance the segmentation effect with the spatio-semantic domain alignment module in manifold cross domain tasks. Furthermore, a pseudo-labels consistency filter is developed for DSSAL-DA to ensure reliability in self-training through cross-view consistency verification. Extensive experiments on two public datasets and a private dataset demonstrate the superiority of DSSAL and DSSAL-DA over the state-of-the-art methods for UDA semantic segmentation of RSIs.