Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery

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

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Change Detection LEVIR-CD ChangeStar(BiSup) F1 91.25 # 13
IoU 83.92 # 9
Overall Accuracy - # 9
Building change detection for remote sensing images LEVIR-CD ChangeStar (FarSeg + ChangeMixin) F1 91.25 # 17
IoU 83.92 # 13
Building change detection for remote sensing images LEVIR-CD ChangeStar (PSPNet + ChangeMixin) F1 87.6 # 36
Building change detection for remote sensing images LEVIR-CD ChangeStar (Semantic FPN + ChangeMixin) F1 90.4 # 26
Building change detection for remote sensing images LEVIR-CD ChangeStar (DeepLab v3+ + ChangeMixin) F1 89.7 # 31
Building change detection for remote sensing images LEVIR-CD ChangeStar (DeepLab v3 + ChangeMixin) F1 87.6 # 36

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