SAS-NET: Similarity Attention Siamese Network for Building Change Detection in UAV Images

Change detection refers to extract change information using deep learning or traditional image processing methods to quantitatively analyze and characterize landmark changes on bi-temporal images. Currently, change detection is mainly a pixel-level task, and obtaining accurate change detection segmentation predictions requires a more elaborate and complex model architecture design. To simplify the change detection task, we proposed a novel similarity detection model, Similarity Attention Siamese Network (SAS-NET). It analyzed and predicted if the bi-temporal image patches were similar, and simplified pixel-level change detection tasks to patch-level similarity classification prediction tasks. In this work, a UAV Similarity Detection Dataset (UAV-SD) was also proposed to explore the advantages of patch-level prediction tasks over pixel-level change detection tasks. The proposed method achieved 90.5% accuracy on UAV-SD, which proves that it is more effective than other advanced change detection methods.