DLC: Dynamic Loss Correction for Cross-Domain Remotely Sensed Segmentation

Due to the diversity of acquisition conditions and imaging mechanisms in remote sensing, the generalization of semantic segmentation models trained with labeled data in the source domain to other unlabeled target domains is hindered. Existing mainstream self-training-based methods provide pseudo-labels to target data as ground truth to utilize target domain evidence for unsupervised domain adaptation (UDA). However, the label shift and domain gap between different domains inevitably introduce noise into pseudo-labeled target data, that is, misclassified pixels. As a consequence, we present a dynamic loss correction (DLC) framework for cross-domain semantic segmentation, which mitigates domain discrepancy by formally modeling the noise distribution of pseudo-labels in the target domain with noise transition matrix (NTM). Specifically, to promote the model output to fit the true label distribution, we employ the high-order consistency information of neighbor representations to estimate NTM and correct the supervision signal without heuristically setting anchors. Furthermore, smooth geometric constraints are introduced to regularize the mutual improvement of NTM derivation and segmentation model optimization in a data-driven manner, thereby compensating for the lack of target domain knowledge. Extensive experimental results on four cross-domain remotely sensed segmentation tasks highlight the generalization capability and competitiveness of the presented method, including cross-scene, cross-band, and cross-modal transfer. Our results and code are available at <uri>https://github.com/heqibin/dlc</uri>.