FERA-Net: A Building Change Detection Method for High-Resolution Remote Sensing Imagery Based on Residual Attention and High-Frequency Features

Abstract

Buildings can represent the process of urban development, and building change detection can support land use management and urban planning. However, existing building change detection models are unable to extract multi-scale building features effectively or fully utilize the local and global information of the feature maps, such as building edges. These defections affect the detection accuracy and may restrict further applications of the models. In this paper, we propose the feature-enhanced residual attention network (FERA-Net) to improve the performance of the ultrahigh-resolution remote sensing image change detection task. The FERA-Net is an end-to-end network with a U-shaped encoder–decoder structure. The Siamese network is used as the encoder with an attention-guided high-frequency feature extraction module (AGFM) extracting building features and enriching detail information, and the decoder applies a feature-enhanced skip connection module (FESCM) to aggregate the enhanced multi-level differential feature maps and gradually recover the change feature maps in this structure. The FERA-Net can generate predicted building change maps by the joint supervision of building change information and building edge information. The performance of the proposed model is tested on the WHU-CD dataset and the LEVIR-CD dataset. The experimental results show that our model outperforms the state-of-the-art models, with 93.51% precision and a 92.48% F1 score on the WHU-CD dataset, and 91.57% precision and an 89.58% F1 score on the LEVIR-CD dataset.