LFHNet: Lightweight Full-Scale Hybrid Network for Remote Sensing Change Detection

The deep learning-based change detection (CD) methods have achieved remarkable progress with remote sensing imagery. These methods mainly rely on complex feature extraction structures and numerous attention mechanisms to realize effective feature extraction and recognition. However, this results in a significant increase in the number of parameters and the training cost of the whole network. The increase in parameterization can also lead to the degradation of network performance when the amount of training data is insufficient. Thus, it is still promising and challenging to perform reliable CD results through light network design. In this article, we propose a lightweight full-scale hybrid network. The network is comprised of a convolutional neural network (CNN), multilayer perceptron (MLP), and transformer, and it is capable of achieving high performance in CD tasks with a lightweight structure. First, the MLP structures are integrated into the basic network to extract global feature information, compensating for the information loss caused by the convolutional operations of CNN. Second, a full-scale difference module is designed to sufficiently extract the feature information and ensure enough feedforward information. Third, a lightweight transformer is appended at the end of the network to accomplish the spatial-temporal correlation of features, which effectively enhances the quality of the final extracted features. Experimental results on three classical CD datasets show that the proposed method outperforms the state-of-the-art methods.