Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
This addresses the problem of expensive labeling for remote sensing change detection, offering a more efficient approach, though it is incremental as it builds on existing segmentation architectures.
The paper tackles the high cost of pairwise labeling for bitemporal change detection in remote sensing imagery by proposing single-temporal supervised learning (STAR), which uses unpaired labeled images as supervisory signals, and shows that their ChangeStar method outperforms baselines with a large margin under this supervision.
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 \textbf{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 supervision and achieves superior performance under bitemporal supervision. Code is available at https://github.com/Z-Zheng/ChangeStar