CAS-Net: Comparison-Based Attention Siamese Network for Change Detection With an Open High-Resolution UAV Image Dataset

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-17
• Main Authors: Zhai, Yikui, Li, Wenba, Xian, Tingfeng, Jia, Xudong, Zhang, Hongsheng, Tan, Zijun, Zhou, Jianhong, Zeng, Junying, Philip Chen, C. L.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Autonomous aerial vehicles; Background noise; Change detection; Change detection (CD); dataset; Datasets; Decoding; Earth surface; Feature extraction; High resolution; Image resolution; Noise; Remote sensing; Siamese network; Task analysis; Transformers; unmanned aerial vehicle (UAV); Unmanned aerial vehicles
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3386918

Change detection (CD) is a process of extracting changes on the Earth's surface from bitemporal images. Current CD methods that use high-resolution remote sensing images require extensive computational resources and are vulnerable to the presence of irrelevant noises in the images. In addressing these challenges, a comparison-based attention Siamese network (CAS-Net) is proposed. The network utilizes contrastive attention modules (CAMs) for feature fusion and employs a classifier to determine similarities and differences of bitemporal image patches. It simplifies pixel-level CDs by comparing image patches. As such, the influences of image background noises on change predictions are reduced. Along with the CAS-Net, an unmanned aerial vehicle (UAV) similarity detection (UAV-SD) dataset is built using high-resolution remote sensing images. This dataset, serving as a benchmark for CD, comprises 10000 pairs of UAV images with a size of 256 \times 256 . Experiments of the CAS-Net on the UAV-SD dataset demonstrate that the CAS-Net is superior to other baseline CD networks. The CAS-Net detection accuracy is 93.1% on the UAV-SD dataset. The code and the dataset can be found at https://github.com/WenbaLi/CAS-Net .