|
Geometric Accuracy Improvement of Geostationary Environment Monitoring Spectrometer by Pixel Offset Adjustment a) Program in Smart City Engineering, Inha University Abstract The Geostationary Environment Monitoring Spectrometer (GEMS) is the world^s first geostationary hyperspectral environmental satellite launched in 2020. It carries out air quality monitoring over the East Asian region by collecting hyperspectral images with spatial resolution of around 7 kms. However, due to factors such as sensor misalignment, attitude instability, and thermal deformation, positional errors can occur in GEMS images. These errors degrade the reliability of products and cause difficulty in time-series analyses and fusion with other satellite data. In this study, we propose a method to evaluate and correct the geometric accuracy of GEMS images using reference images with spatial resolution of 1 km from the Advanced Meteorological Imager (AMI), a geostationary meteorological satellite. First, spatial collocation is performed between GEMS and AMI images to match their spatial resolution and acquisition time. Then, the collocated GEMS image is shifted within a \pm5 pixel range in vertical and horizontal directions to perform global matching. Cross-correlation is computed for each shift condition and the shift with the highest correlation is defined as the optimal offset. This offset is applied to the GEMS image. Finally, correlation is recalculated on the adjusted image to evaluate geometric accuracy before and after correction. The experiment was carried out using daily collected GEMS and AMI images from January 1, 2023 to May 31, 2025. Results showed that average pixel root mean squared error (RMSE) decreased from 1.36 pixels to 0.69 pixels. In most cases, geometric errors were reduced to within 1 pixel. These results confirmed that geometric accuracy of GEMS could be effectively improved by ptimal pixel shift offsets derived from the matching between GEMS and AMI images. Keywords: Satellite Image- Geometric Correction- GEMS- AMI- Image Matching Topic: Topic A: General Remote Sensing |
| ACRS 2025 Conference | Conference Management System |