Confidence-Guided Joint Complementary Learning for Weakly Annotated Remote Sensing Object Segmentation

Yanan Liu, Libao Zhang

Abstract: Object segmentation from weakly annotated remote sensing images (RSIs) is an essential task that helps substantially reduce pixelwise labeling costs. Although mainstream multistage methods have achieved great performance, they generally suffer from high implementation complexity, which limits their practical application. On the other hand, for a single-stage scheme that can be trained in one cycle, error accumulation during joint optimization results in performance degradation. To address these issues, we propose a confidence-guided joint complementary learning (CGJCL) framework for remote sensing object segmentation under image-level annotations that can be easily trained in a single stage and achieve satisfactory performance. CGJCL integrates the localization and segmentation network into a unified framework that employs a joint complementary learning strategy, collaboratively enhancing the performance of each subnetwork. First, a confidence-guided pseudolabel refinement (CGPLR) module is developed to fuse the complementary semantic information of the localization clues and the object boundary/structure details learned from each subnetwork to generate high-quality pseudolabels (PLs) and alleviate error accumulation. Second, a dual self-supervised multiscale consistency (DSMC) loss is presented to both explicitly and implicitly utilize consistency regularization, enabling the promotion of model robustness on multiscale objects and preventing overfitting on false-annotated pixels in noisy PLs. Third, a locally enhanced feature aggregation network is proposed to integrate the multilevel semantic features and alleviate low-level noise interference, producing precise segmentation masks. Extensive evaluation of three RSI datasets demonstrates that the proposed method yields superior performance compared with recent single-stage techniques and several multistage methods, thus revealing its effectiveness and superiority.