The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
A deep learning framework for remote sensing image registration
Abstract
Abstract We propose an effective deep neural network aiming at remote sensing image registration problem. Unlike conventional methods doing feature extraction and feature matching separately, we pair patches from sensed and reference images, and then learn the mapping directly between these patch-pairs and their matching labels for later registration. This end-to-end architecture allows us to optimize the whole processing (learning mapping function) through information feedback when training the network, which is lacking in conventional methods. In addition, to alleviate the small data issue of remote sensing images for training, our proposal introduces a self-learning by learning the mapping function using images and their transformed copies. Moreover, we apply a transfer learning to reduce the huge computation cost in the training stage. It does not only speed up our framework, but also get extra performance gains. The comprehensive experiments conducted on seven sets of remote sensing images, acquired by Radarsat, SPOT and Landsat, show that our proposal improves the registration accuracy up to 2.4–53.7%.
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Cited by
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
A review of multimodal image matching: Methods and applications
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A Deep Learning Semantic Template Matching Framework for Remote Sensing Image Registration
Li, L., Han, L., Ding, M., Cao, H., & Hu, H. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.09.012 · Cited by 67
Open-ended remote sensing visual question answering with transformers
Al Rahhal, M. M., Bazi, Y., Alsaleh, S. O., Al-Razgan, M., Mekhalfi, M. L., Al Zuair, M., & Alajlan, N. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2145583 · Cited by 15
Metadata
- Authors
- Wang, S., Quan, D., Liang, X., Ning, M., Guo, Y., & Jiao, L.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 148-164
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2781778455
- Cited by
- 292
- References
- 66
- FWCI
- 25.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- National Natural Science Foundation of China (61771379, 61571342)
- Japan Society for the Promotion of Science (15K00236)
- Higher Education Discipline Innovation Project (B07048)
- Program for Changjiang Scholars and Innovative Research Team in University (IRT_15R53)
- National Key Research and Development Program of China (2013CB329402)
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- Wang, S., Quan, D., Liang, X., Ning, M., Guo, Y., & Jiao, L. (2018). A deep learning framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2017.12.012
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