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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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References

  1. Wu Y, Li J, Yuan Y, et al. Commonality autoencoder: learning common features for change detection from heterogeneous images. IEEE Trans Neural Netw Learn Syst, 2022, 33: 4257–4270

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 62076204).

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710129, China

    Jiao Shi, Tiancheng Wu & Yu Lei

  2. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Hanwen Yu

  3. Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, 3122, Australia

    A. K. Qin

  4. Department of Embedded Systems Engineering, Incheon National University, Incheon, 22012, Republic of Korea

    Gwanggil Jeon

Authors
  1. Jiao Shi
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  2. Tiancheng Wu
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  3. Hanwen Yu
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  4. A. K. Qin
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  5. Gwanggil Jeon
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  6. Yu Lei
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Corresponding author

Correspondence to Yu Lei.

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Supporting information

Appendixes A and B. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Supplementary File: Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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Shi, J., Wu, T., Yu, H. et al. Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images. Sci. China Inf. Sci. 66, 140308 (2023). https://doi.org/10.1007/s11432-022-3693-0

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  • DOI: https://doi.org/10.1007/s11432-022-3693-0

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