An Extremely Lightweight Change Detection Algorithm Based on Light Global-Local Feature Enhancement Module

Remote sensing image change detection refers to finding the changed regions from a pair of registered images. It has important applications in many fields. However, most methods based on convolutional neural networks and transformers have high complexity and cannot be effectively deployed on satellites or drones in practical applications. To address this issue, we propose an extremely lightweight change detection algorithm called ELW_CDNet. Its inference speed is very fast. This method is based on the extremely lightweight shufflenetv2. Moreover, considering that both global as well as local features play an important role in change detection, we design a light global-local feature enhancement module (LGLFEM) for reinforcing the features extracted by the backbone. Specifically, the global feature extraction module in LGLFEM is implemented using separable self-attention (SSA), which has linear complexity and very low computational effort. We conduct experiments on two change detection datasets. Compared with some state-of-the-art methods, the proposed method can achieve superior performance with extremely fast inference speed. On the LEVIR-CD dataset, it achieves an F1 score of 90.47%, an intersection over union (IoU) of 82.60%, and a frame per second (FPS) of 914 with 1.75 M parameters and 1.91 GFLOPs. The code will be released soon on the site of https://github.com/dyl96/ELW_CDNet.