A Comprehensive Evaluation of Deep Learning-Based Image Super-Resolution for GCP Chip Matching Using High-Resolution Satellite Imagery.
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| Title: | A Comprehensive Evaluation of Deep Learning-Based Image Super-Resolution for GCP Chip Matching Using High-Resolution Satellite Imagery. |
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| Authors: | Chung, Minkyung1 (AUTHOR), Seo, Doochun2 (AUTHOR), Jung, Chanyeop1 (AUTHOR), Han, Youkyung1,2 (AUTHOR) han602@seoultech.ac.kr |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3104. 34p. |
| Subjects: | Deep learning, Spatial resolution, Template matching (Digital image processing), High resolution imaging, Image registration, Remote-sensing images |
| Abstract: | Highlights: What are the main findings? Deep learning-based image super-resolution improves satellite-derived GCP chip matching, particularly for low-resolution GCP chips with limited spatial detail. The impact of SR varies depending on the spatial characteristics of input GCP chips, their resolution relative to the target imagery, and the adopted template matching method. What are the implications of the main findings? SR-enhanced satellite-derived GCP chips have the potential to serve as a practical alternative to aerial GCP chips for satellite image registration when aerial imagery is unavailable. The findings provide practical guidance for applying SR to satellite-derived GCP chips by clarifying when SR can effectively improve their usability for registration applications. High-resolution (HR) Ground Control Point (GCP) chips are essential for accurate satellite image registration, yet their generation commonly relies on aerial imagery, which is often limited by data availability and update frequency. To improve the usability of satellite-derived GCP chips as an alternative, this study systematically investigates the influence of deep learning-based image super-resolution (SR) on GCP chip template matching under varying input spatial resolution conditions. GCP chips extracted from KOMPSAT-3 imagery were synthetically degraded using ×4 and ×2 downsampling, while original-resolution chips (×1) were also included for comparison. The degraded chips were enhanced using SR models and matched to KOMPSAT-3A imagery using both conventional and deep learning-based template matching methods. The experimental results show that SR is more effective for input GCP chips with limited spatial detail, with the largest improvements obtained under the ×4 downsampling condition (~2.8 m GSD). The contribution of SR is further influenced by geometric resampling during template matching and by the adopted matching method. These findings identify the conditions under which SR provides meaningful improvements and offer practical guidance for enhancing satellite-derived GCP chips, extending their usability for satellite image registration when aerial imagery is unavailable or requires frequent updates. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Deep learning-based image super-resolution improves satellite-derived GCP chip matching, particularly for low-resolution GCP chips with limited spatial detail. The impact of SR varies depending on the spatial characteristics of input GCP chips, their resolution relative to the target imagery, and the adopted template matching method. What are the implications of the main findings? SR-enhanced satellite-derived GCP chips have the potential to serve as a practical alternative to aerial GCP chips for satellite image registration when aerial imagery is unavailable. The findings provide practical guidance for applying SR to satellite-derived GCP chips by clarifying when SR can effectively improve their usability for registration applications. High-resolution (HR) Ground Control Point (GCP) chips are essential for accurate satellite image registration, yet their generation commonly relies on aerial imagery, which is often limited by data availability and update frequency. To improve the usability of satellite-derived GCP chips as an alternative, this study systematically investigates the influence of deep learning-based image super-resolution (SR) on GCP chip template matching under varying input spatial resolution conditions. GCP chips extracted from KOMPSAT-3 imagery were synthetically degraded using ×4 and ×2 downsampling, while original-resolution chips (×1) were also included for comparison. The degraded chips were enhanced using SR models and matched to KOMPSAT-3A imagery using both conventional and deep learning-based template matching methods. The experimental results show that SR is more effective for input GCP chips with limited spatial detail, with the largest improvements obtained under the ×4 downsampling condition (~2.8 m GSD). The contribution of SR is further influenced by geometric resampling during template matching and by the adopted matching method. These findings identify the conditions under which SR provides meaningful improvements and offer practical guidance for enhancing satellite-derived GCP chips, extending their usability for satellite image registration when aerial imagery is unavailable or requires frequent updates. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18183104 |
