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

• URL: http://arxiv.org/abs/2108.07002v3
• Date: Sat, 14 Oct 2023 03:18:11 GMT
• Title: Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery
• Authors: Zhuo Zheng, Ailong Ma, Liangpei Zhang, Yanfei Zhong
• Abstract summary: We propose single-temporal supervised learning (STAR) for change detection from a new perspective. We train a high-accuracy change detector only using textbfunpaired labeled images and generalize to real-world bitemporal images. ChangeStar outperforms the baseline with a large margin under single-temporal supervision and achieves superior performance under bitemporal supervision.
• Score: 23.620965329033716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \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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