Bilateral Attention U-Net with Dissimilarity Attention Gate for Change Detection on Remote Sensing Imageries

Abstract

This study proposes a bilateral attention U-Net with a dissimilarity attention gate (DAG) for change detection on remote sensing imageries. The proposed network is designed with a bilateral dissimilarity encoding for the DAG calculation to handle reversible input images, resulting in high detection rates regardless of the order of the two input images for change detection. The DAG exploits all the combinations of joint features to avoid spectral information loss fed into an attention gate on the decoder side. The effectiveness of the proposed method was evaluated on the KOMPSAT-3 satellite images dataset and the aerial change detection dataset (CDD). Its performance was better than that of conventional methods (specifically, U-Net, ATTUNet, and Modified-UNet++) as it achieved average F1-score and kappa coefficient (KC) values of 0.68 and 66.93, respectively, for the KOMPSAT-3 dataset. For CDD, it achieved F1-score and KC values of 0.70 and 68.74, respectively, which are also better values than those achieved by conventional methods. In addition, we found that the proposed bilateral attention U-Net can provide the same changed map regardless of whether the image order is reversed.

Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images

“…In recent years, the deep learning model [11][12][13][14][15][16][17][18] with powerful feature representation capability has gradually taken its place in the field of change detection. Deep learningbased change detection methods feed input image pairs into neural networks to extract features.…”