NATCD: A Multi-Scale Neighborhood Attention Transformer Network for Remote Sensing Image Change Detection

Zhixiang Guo, Hao Chen, Fachuan He

Abstract: To address the challenge of high computational cost on transformer-based network for change detection (CD) with high-resolution remote sensing images, a neighborhood attention transformer (NAT) based on multi-scale feature fusion method (NATCD) is proposed. Initially, to effectively extract neighborhood features while reducing model complexity, a hierarchical NAT encoder is constructed for multi-scale feature extraction of bi-temporal remote sensing images. Secondly, to associate multi-scale features and alleviate the issue of poor inter-neighborhood feature correlation caused by neighborhood attention operations, a feature fusion decoder is constructed for predicting binary change maps. Experiment on LEVIR-CD and WHU-CD datasets show that NATCD achieves a better performance with a significantly computational cost than previous methods.