HMCNet: Hybrid Efficient Remote Sensing Images Change Detection Network Based on Cross-Axis Attention MLP and CNN

Abstract

As an important task in the field of remote sensing image interpretation, change detection (CD) has been extensively studied by scholars in recent years. Affected by the illumination and the environment during bitemporal images' acquisition, there will be many pseudochanges, and the pseudochanges will seriously affect the effect of CD. Based on this, we propose a CD model named HMCNet, which introduces multilayer perceptron (MLP) into a convolutional neural network (CNN)-based CD model to form an MLP-CNN hybrid model. HMCNet has both the good feature extraction of CNN and the long-term dependence modeling ability of MLP, which can effectively overcome the interference of pseudochanges. In addition, the proposed cross-axis attention MLP can induce window attention of local features through shifted windows and, at the same time, form global attention to features through the interaction between information flows on the cross-axis, which effectively improves the comprehensive performance of MLP block. Extensive experiments on three public benchmark datasets show that HMCNet can achieve better performance with fewer parameters and Flops, and still maintain good generalization ability with fewer train data.