Kwan-Young Oh

Automated Bias-Compensation Approach for Pushbroom Sensor Modeling Using Digital Elevation Model

Bias compensation of rational polynomial coefficients (RPCs) is one of the most important preprocessing steps in high-resolution satellite image processing. It generally requires accurate ground control points (GCPs), but GCP acquisition is both time consuming and laborious. In this paper, we propose a time- and cost-efficient method for automated bias compensation of the RPC of high-resolution stereo image pairs. Two Korean Multi-purpose Satellite-2 (KOMPSAT-2) stereo image pairs acquired in Daejeon and Busan, Korea, and the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) with the spatial resolution of 3 arcsec (~90 m) were used for analysis. In the two study areas, 33 and 29 check points were respectively used for the performance evaluation. After bias compensation with the proposed method, the root-mean-square (RMS) errors for both of the study areas were less than 10 m, in all coordinate components, while the RMS error vectors were approximately 10 m. Although the RMS error vectors were slightly larger than the standard deviations of the residual errors of the initial ground coordinates, it would seem that they yielded acceptable values because the proposed method largely depends on the spatial resolution, the error of the SRTM DEM, the tie point selection error, and so on. Therefore, it can be concluded that the proposed method allows for the automated bias compensation of RPCs of KOMPSAT-2 images.

Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology

The purpose of this study was to analyze geospatial information (GI) research trends using text-mining techniques. Data were collected from 869 papers found in the Korea Citation Index (KCI) database (DB). Keywords extracted from these papers were classified into 13 GI domains and 13 research domains. We conducted basic statistical analyses (e.g., frequency and time series analyses) and network analyses, using such measures as frequency, degree, closeness centrality, and betweenness centrality, on the resulting domains. We subdivided the most frequent GI domain for more detailed analysis. Such processes permit an analysis of the relationships between the Research Fields associated with each GI. Our time series analysis found that the Climate and Satellite Image domain frequencies continuously increased. Satellite Image, General-Purpose Map, and Natural Disaster Map in the GI domain and Climate and Natural Disaster in the Research Field domain appeared in the center of the GI-Research Field network. We subdivided the Satellite Image domain for detailed analysis. As a result, LANDSAT, KOMPSAT, and MODIS displayed high scores on the frequency, degree, closeness centrality, and betweenness centrality indices. These results will be useful in GI-based interdisciplinary research and the selection of new research themes.