LMG-Net: A Lightweight Remote Sensing Change Detection Network With Multilevel Global Features

Remote sensing change detection (RSCD) is a key tool for environmental monitoring and resource management, playing a significant role in monitoring dynamic surface changes. In practical applications, RSCD often requires high precision and efficient detection methods. However, traditional methods tend to involve high technical complexity and a large number of parameters, and are susceptible to interference from complex background noise, leading to poor performance in detecting change areas. To address these issues, this paper proposes a lightweight remote sensing change detection network, LMG-Net. The model uses a lightweight encoder and incorporates a Hierarchical Transformer Module (HTF) to suppress background noise and minimize parameter increase, effectively extracting multi-level global features. Additionally, the paper introduces a Multi-dimensional Cooperative Attention Guidance (MAG) mechanism, further enhancing the ability to detect boundary changes. The model has only 3.29M parameters and a computational load of 3.89G, demonstrating its high applicability, particularly for real-time applications in resource-constrained environments. Experimental results show that LMG-Net achieves state-of-the-art F1 scores and IoU values on the WHU-CD, SYSU-CD, and LEVIR-CD+ datasets: (94.79%, 90.09%), (82.29%, 69.90%), and (84.30%, 71.14%).