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.
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GANs are applied to image matching and registration.
“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
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- 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 › Engineering › Ocean Engineering › 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
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