GEO DATA

Data Articles

GeoAI Dataset for Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery: Eu-Ru Lee, Jun-Hyeok Jung, Ki-Chang Kim, Seong-Jae Yu, Hyung-Sup Jung; GEO DATA. 2024;6(4):435-450. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0046

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Abstract PDF: This study presents the generation of a GeoAI dataset for urban water body detection using TerraSAR-X satellite synthetic aperture radar (SAR) imagery. The study area includes urban regions in Seoul and Gyeonggi Province, chosen for their complex structures and frequent flooding, which pose challenges for SAR analysis. The data preprocessing involved generating Sigma0 images, image co-registration, median filtering for speckle noise reduction, decibel conversion, and orthorectification using Copernicus DEM for precise geometric correction. Label data were created using the global river widths from Landsat dataset combined with the Otsu thresholding method and fine-tuned with Google Map imagery. Annotation guidelines were meticulously designed to account for SAR-specific phenomena such as layover, corner reflections, and side lobe effects, ensuring consistent and accurate labeling across different orbits and observation conditions. The resulting dataset supports deep learning models in learning geometric characteristics of SAR imagery, enhancing water body detection capabilities. This work provides a foundational resource for future applications in urban water management and climate-resilient disaster response.

Evaluation of Residual Phase from Orbit Accuracy Using TerraSAR-X/TanDEM-X SAR Observation: Yeojin Kim, Sang-Hoon Hong; GEO DATA. 2024;6(4):487-494. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0039

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Abstract PDF: Interferometric synthetic aperture radar (InSAR) is used to observe precise surface displacement and create digital elevation models by calculating the phase differences between two or more SAR images obtained over the same surface area. The phase of a repeat-pass interferogram can be expressed as the sum of contributions from topography, ground displacement, earth curvature, noise, and the satellite’s orbital phase component. For precise observations, removing unnecessary phase components is essential. Errors owing to the satellite’s orbit accuracy leave residual phases in the interferogram, which become a significant limitation for wide-area ground displacement monitoring using the InSAR technique. This study used four pairs of images acquired by TerraSAR-X in monostatic pursuit mode from October 2014 to February 2015 to analyze the residual phase caused by orbital errors. Since these images were acquired with a 10-second interval between the TerraSAR-X and TanDEM-X satellites, the phase coherence was maintained over time. The Tarim Basin in China was selected as the study area to minimize the impact of terrain distortion. By introducing a 0.5 m error into the x, y, and z components of the satellite position vectors and creating differential interferograms, it was found that the x component’s orbital error caused the largest residual phase, with linear residual phases observed in the north-south direction. Furthermore, various baselines ranging from -29.71 to 263.21 m were used to quantitatively compare the residual phases caused by orbital errors based on the perpendicular baseline. The residual phase was similar across the four differential interferograms, with approximately 3.49 π for the x component, 0.85 π for the y component, and 1.25 π for the z component. The residual phase resulting from simulated orbital errors was effectively mitigated using a 2D quadratic model.

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