ELFFNet: An Efficient Lightweight Feature Fusion Network for Change Detection

Abstract

Remote sensing change detection is essential for land use planning, urban monitoring, and disaster response. However, conventional deep learning methods often suffer from blurred building and vegetation edges, shadow misclassification, and low efficiency due to excessive complexity. To address these issues, we propose ELFFNet, a lightweight and efficient network that achieves high detection accuracy with reduced computational cost. ELFFNet integrates Asymmetric Dilated Convolution Modules for deep semantic extraction, Multi Efficient Attention Modules between encoder and decoder to retain fine spatial details, and a simplified Pyramid Pooling Module at the deepest stage for low-cost global context aggregation. Experiments on the GVLM-CD and WHU-CD data sets show superior performance, with Kappa coefficients of 82.86% and 94.48%, F1 scores of 91.72% and 95.24%, and mean intersection over union values of 84.96% and 94.73%. Importantly, ELFFNet requires a parameter size of 7.31 MB and 22.6 giga floating-point operations [GFLOPs])—far fewer than Transformer-based models—while delivering strong edge detection for small targets and complex disaster areas. This balance of accuracy and efficiency makes ELFFNet highly suitable for practical remote sensing applications.