The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching
Abstract
Tasks such as the monitoring of natural disasters or the detection of change highly benefit from complementary information about an area or a specific object of interest. The required information is provided by fusing high accurate coregistered and georeferenced datasets. Aligned high-resolution optical and synthetic aperture radar (SAR) data additionally enable an absolute geolocation accuracy improvement of the optical images by extracting accurate and reliable ground control points (GCPs) from the SAR images. In this paper, we investigate the applicability of a deep learning based matching concept for the generation of precise and accurate GCPs from SAR satellite images by matching optical and SAR images. To this end, conditional generative adversarial networks (cGANs) are trained to generate SAR-like image patches from optical images. For training and testing, optical and SAR image patches are extracted from TerraSAR-X and PRISM image pairs covering greater urban areas spread over Europe. The artificially generated patches are then used to improve the conditions for three known matching approaches based on normalized cross-correlation (NCC), scale-invariant feature transform (SIFT), and binary robust invariant scalable key (BRISK), which are normally not usable for the matching of optical and SAR images. The results validate that a NCC-, SIFT-, and BRISK-based matching greatly benefit, in terms of matching accuracy and precision, from the use of the artificial templates. The comparison with two state-of-the-art optical and SAR matching approaches shows the potential of the proposed method but also revealed some challenges and the necessity for further developments.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, the GANs are applied to image matching and registration (Merkle et al., 2018;Hughes et al., 2018b).”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Merkle et al., 2018
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
(2017) · International Conference on Machine Learning · Cited by 6,382
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
Research works printed but not linked: 1
- tieleman, 2012, Lecture 6.5-RMSProp: Divide the gradient by a running average of its recent magnitude, Neural Networks for Machine Learning
Cited by
Sorted by citations
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
Sun, A. Y., & Scanlon, B. R. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab1b7d · Cited by 585
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 561
Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives
Zhu, X. X., Montazeri, S., Ali, M., Hua, Y., Wang, Y., Mou, L., Shi, Y., Xu, F., & Bamler, R. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2020.3046356 · Cited by 422
THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSION
Schmitt, M., Hughes, L. H., & Zhu, X. X. (2018) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-1-141-2018 · Cited by 374
Metadata
- Authors
- Merkle, N., Auer, S., Muller, R., & Reinartz, P.
- Affiliations
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2018-06
- Volume
- 11
- Issue
- 6
- Pages
- 1811-1820
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2786355301
- Cited by
- 159
- References
- 37
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 17 · 2023: 22 · 2022: 25 · 2021: 35 · 2020: 17 · 2019: 16 · 2018: 5
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- elib (German Aerospace Center) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- conditional generative adversarial networks, synthetic aperture radar, ground control points, TerraSAR-X, PRISM, normalized cross-correlation, SIFT, BRISK, image registration, georeferencing, change detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
