Elaborate Information Refinement Network for Fine-Grained Object Detection in Remote Sensing Images

作者：J. F. Sun, Xi Yang, Dong Yang · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3624695 · 被引用次数：7 · 研究领域：Infrared Target Detection Methodologies、Remote-Sensing Image Classification、Advanced Measurement and Detection Methods

0pt Fine-grained object detection aims to localize and classify subcategories of objects by extracting more discriminative semantic features, which is particularly challenging for remote sensing images due to their complex backgrounds and arbitrarily oriented objects. Accurate localization with bounding box regression typically relies on detailed texture and edge information to delineate object boundaries, while fine-grained classification requires more elaborate semantic information. However, existing methods often share the same input features across the model, resulting in a mismatch between the requirements of the localization task and those of the fine-grained classification task. To address this problem, we propose a Elaborate Information Refinement Network (EIRNet), which not only effectively separates features for localization and fine-grained classification but also refines these features according to the specific requirements of each task. For fine-grained classification, we propose a Fine-grained Context Fusion Module (FCFM) to enhance the ability to extract discriminative features by expanding the receptive field. For localization, we introduce an Edge Information Sensing Module (EISM) to extract scale-invariant features by combining high-dimensional information with detailed edge information, thereby improving the network’s ability to accurately locate objects. Additionally, to extract richer fine-grained semantic information, we present a Feature Injection Modul...