Urban localization based on aerial imagery by correcting projection distortion
Author:
Funder
KAIST Key Research Institutes Project
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10514-022-10082-5.pdf
Reference48 articles.
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2. Audebert, N., Le Saux, B., & Lefèvre, S. (2018). Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 20–32. https://doi.org/10.1016/j.isprsjprs.2017.11.011
3. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
4. Cai, S., Guo, Y., Khan, S., Hu, J., & Wen, G. (2019). Ground-to-aerial image geolocalization with a hard exemplar reweighting triplet loss. In Proceedings of the IEEE international conference on computer vision (pp. 8391–8400). https://doi.org/10.1109/iccv.2019.00848
5. Chen, H., Zhang, K., Xiao, W., Sheng, Y., Cheng, L., Zhou, W., & Zhang, S. (2021). Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification. International Journal of Remote Sensing, 42(7), 2686–2705. https://doi.org/10.1080/01431161.2020.1862437
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