RLRAnet: Reinforcement Learning-Guided Reverse Attention Network for Remote Sensing Tubular Structure

Segmentation of tubular structures in remote sensing imagery represents a domain of significant value for geographic information systems. However, achieving high-quality automated segmentation remains challenging due to morphological diversity and boundary ambiguity between tubular structures and their backgrounds. To address these challenges, we propose RLRAnet, an enhanced Unet-based segmentation architecture that incorporates boundary-aware mechanisms to improve tubular structure extraction. Specifically, we designed a reverse Attention Module that performs inverse calibration on low-level features within multi-scale skip connections, thereby enhancing boundary detail representation. Additionally, we introduced a reinforcement learning-based dynamic loss weight adjustment strategy that leverages a policy network to adaptively balance Soft Dice loss and boundary loss, achieving optimal reconciliation between global segmentation and local detail preservation. Extensive evaluations on two distinct remote sensing tubular structure datasets demonstrate that RLRAnet significantly outperforms multiple advanced segmentation architectures across IoU, Accuracy, Recall, and F1 Score, substantiating its superior performance in tubular structure segmentation tasks.