A Multi-Scale Feature Enhancement and Attention-Guided Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD) plays a critical role in disaster assessment, land use monitoring, and environmental analysis. Despite notable progress with deep learning, especially convolutional neural networks (CNNS) and transformer-based models, existing approaches still face challenges such as false detections in multi-scale objects and cross-scale semantic inconsistencies. To address these limitations, we propose DMF-CDNet, a dual-stream multi-scale fusion change detection built on a resnet-18 backbone. The model integrates two key modules: the feature-enhanced spatial-spectral feature coordination (FE-SSFC) module, which combines pyramid split attention module with residuals(PSAR)-based multi-scale convolution and statistically guided enhancement to highlight true changes and suppress noise, and the dual-branch decoding module (DDM), which incorporates guidance and channel rearrangement strategies to improve semantic consistency and boundary preservation. Through progressive decoding and cross-scale fusion, the network achieves more accurate localization of change regions and finer boundary recovery. Experimental results on the LEVIR-CD, HRCUS-CD, and SYSU-CD datasets demonstrate that DMF-CDNet achieves F1-scores of 91.52%, 74.92%, and 81.96%, respectively, confirming its effectiveness in multi-scale modeling and fine-grained change detection for complex RSCD scenarios.