IUR-Net: A Multi-Stage Framework for Label Refinement Tasks in Noisy Remote Sensing Samples - Yibing Xiong; Xiangyun Hu; Xin Geng; Lizhen Lei; Aokun Liang (Remote Sensing, 2025)
Currently, samples are a critical driving force in the application of deep learning. However, the use of samples encounters problems, such as an inconsistent annotation quality, mismatches between images and labels, and a lack of fine-grained labels. Refining sample labels is essential for training a sophisticated model. Refining sample labels through manual verification is labor-intensive, especially for training large models. Additionally, existing label refinement methods based on deep neural networks (DNNs) typically rely on image features to directly predict segmentation results, often overlooking the potential information embedded in existing noisy labels. To address these challenges and shortcomings, this study proposes a novel remote sensing sample label refinement (LR) network, named the identify–update–refine network (IUR-Net). IUR-Net leverages newly acquired remote sensing images and their corresponding noisy labels to automatically identify erroneous regions, update them with more accurate information, and refine the results to improve label quality. A multi-scale, error-aware localization module (Ms-EALM) is designed to capture label–image inconsistencies, enabling the more accurate localization of erroneous label regions. To evaluate the proposed framework, we first constructed and publicly released two benchmark datasets for the label refinement task: WHU-LR and EVLAB-LR. The experimental results on these datasets demonstrate that the labels refined by IUR-Net not only outperform the baseline model in both IoU and F1 scores, but also effectively identify errors in noisy annotations.