Remote Sensing Image Change Detection based on Cross Mixing Attention Network

Abstract

Abstract Change detection is a crucial undertaking in the field of remote sensing. Current change detection methods tend to emphasize modelling difference features, ignoring the alignment error of dual-temporal images and the spatio-temporal relationship between dual-temporal images, which affects the recognition ability of features and makes it difficult to distinguish the real change region. Aiming at the above problems, this paper proposes a remote sensing image change detection method based on cross mixing attention network. The method employs the feature alignment module to obtain dual-temporal correction features to improve the classification effect of the boundary pixels of the target region. The spatio-temporal relationship of the dual-temporal phase images is better exploited by the cross mixing attention module to obtain attention maps at different scales to guide the up-sampling and enhancing the detection performance of target areas at different scales. Our introduced network demonstrates promising performance, as evidenced by extensive experimental results on both the LEVIR-CD dataset and SYSU-CD dataset.