Fusion of UNet and ResNet decisions for change detection using low and high spectral resolution images
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03185-2.pdf
Reference23 articles.
1. Khelifi, L., Mignotte, M.: Deep learning for change detection in remote sensing images: comprehensive review and meta-analysis. Ieee Access 8, 126385–126400 (2020). https://doi.org/10.1109/ACCESS.2020.3008036
2. Shi, J., et al.: Unsupervised Multiple Change Detection in Remote Sensing Images via Generative Representation Learning Network. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021). https://doi.org/10.1109/LGRS.2021.3085022
3. Fang, H., Du, P., Wang, X.: A novel unsupervised binary CD method for VHR optical remote sensing imagery over urban areas. J. Appl. Earth Observ. Geoinform. 108, 102749 (2022). https://doi.org/10.1016/j.jag.2022.102749
4. Shi, J., et al.: Fine object change detection based on vector boundary and deep learning with high-resolution remote sensing images. IEEE J. Appl. Earth Observ. Remote Sens. 15, 4094–4103 (2022). https://doi.org/10.1109/JSTARS.2022.3168691
5. Pati, C., et al.: A novel hybrid machine learning approach for change detection in remote sensing images. Eng. Sci. Technol. 23(5), 973–981 (2020). https://doi.org/10.1016/j.jestch.2020.01.002
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