Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
Zhuo Zheng, Ailong Ma, Liangpei Zhang, Yanfei Zhong
Abstract
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label large-scale bitemporal HSR remote sensing images. In this paper, we propose single-temporal supervised learning (STAR) for change detection from a new perspective of exploiting object changes in unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and generalize to real-world bitemporal images. To evaluate the effectiveness of STAR, we design a simple yet effective change detector called ChangeStar, which can reuse any deep semantic segmentation architecture by the ChangeMixin module. The comprehensive experimental results show that ChangeStar outperforms the baseline with a large margin under single-temporal super-vision and achieves superior performance under bitemporal supervision. Code is available at https://github.com/Z-Zheng/ChangeStar.