Sand ridge lines serve as key geomorphological indicators for interpreting aeolian dynamics and assessing desertification intensity. However, automated extraction of continuous ridge structures from remote sensing imagery remains challenging due to the multi-scale morphology of dunes, complex surface textures, and strong shadow interference. Conventional edge detection models often rely on computationally heavy backbones or suffer from structural discontinuities in subtle ridge branches, limiting their applicability in large-scale desert monitoring. To address these challenges, we propose Rid-HRNet, a lightweight high-resolution network specifically designed for efficient and structurally coherent sand ridge extraction. Unlike traditional encoder-decoder architectures, Rid-HRNet maintains parallel high-resolution representations throughout the network to preserve fine spatial details. A Multi-Scale Information Aggregation (MSIA) module enhances cross-scale feature interaction by integrating shallow structural cues with deeper semantic representations. In addition, an Improved Contextual Fusion Module (ICFM) employs pixel-wise attention to adaptively fuse multi-level predictions, reinforcing ridge continuity while suppressing background interference. Experiments on Landsat-8 desert imagery demonstrate that Rid-HRNet achieves an Optimal Dataset Scale (ODS) of 0.790, an Optimal Image Scale (OIS) of 0.806, an Average Precision (AP) of 0.710, and an AC(R50) score of 0.744. The proposed model outperforms classical VGG-based detectors, including HED and RCF, as well as recent lightweight baselines such as PiDiNet and LDC, in terms of overall accuracy and structural consistency. Notably, Rid-HRNet contains only 0.20M parameters and requires 0.55 GFLOPs, operating at 279.23 FPS with a GPU memory footprint of 0.02 GB. These results indicate that Rid-HRNet achieves a favorable balance between detection performance and computational efficiency, supporting large-scale geomorphological mapping and operational desert monitoring based on high-resolution satellite imagery.