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Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection

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Abstract

Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios.

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Data Availibility Statement

The datasets analyzed during this study are all publicly available for research purpose - the xView2, SpaceNet 8, LEVIR-CD, WHU-CD, DynamicEarthNet, CDD, S2Looking, and SECOND datasets.

Code Availability

Code is available at https://github.com/Z-Zheng/pytorch-change-models.

Notes

  1. The bitemporal pixels should be at the same geographical position.

  2. https://z-zheng.github.io/assets/changestar2/DEN/val_star.txt

  3. https://z-zheng.github.io/assets/changemask/SECOND/train.csv

  4. https://z-zheng.github.io/assets/changemask/SECOND/val.csv

  5. http://www.captain-whu.com/project/SCD/.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant No. 42325105.

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Authors and Affiliations

  1. LIESMARS, Wuhan University, Wuhan, China

    Zhuo Zheng, Yanfei Zhong, Ailong Ma & Liangpei Zhang

  2. Department of Computer Science, Stanford University, Stanford, USA

    Zhuo Zheng

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  1. Zhuo Zheng
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  2. Yanfei Zhong
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Correspondence to Yanfei Zhong.

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Communicated by Takayuki Okatani.

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Zheng, Z., Zhong, Y., Ma, A. et al. Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection. Int J Comput Vis 132, 5582–5602 (2024). https://doi.org/10.1007/s11263-024-02141-4

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