Geometric Correction of Large-Scale KOMPSAT-3A Images through RPCs Re-Adjustment: Optimization of Bundle Block Adjustment Based on Reference Images

초록

Recently, as the frequency of high-resolution satellite image collection and the amount of data have increased, the demand for image data with temporal and spatial accuracy has been rising in various fields. This image data serves an important role in various applications such as environmental monitoring, urban planning, and disaster management. However, geometric correction using ground control points for individual images is inefficient in terms of time and cost, and its practicality decreases, especially when processing large numbers of images. In this study, we propose a method to efficiently re-estimate the rational polynomial coefficients (RPCs) correction coefficients for a large number of uncorrected KOMPSAT-3/3A images by setting a specific orthorectified CAS500-1 as the reference image and using bundle block adjustment. The proposed method applies the inverse geocoding technique to the set reference image to reproduce the image corresponding to the Level-2 Radiometric (L2R) and the RPCs information. Afterward, bundle block adjustment is performed with other Level-1 Radiometric (L1R) or L2R images to re-estimate the RPCs correction coefficients in bulk. This process improves the geometric correction accuracy for a large number of images while also saving time compared to the method of correcting each image independently. As a result of experiments using the proposed methodology, the initial relative error position error was reduced from 160 pixels to 1.3 pixels. This demonstrated significant improvement in accuracy and efficiency performance. Through the proposed method, high-accuracy single satellite images were used to perform precise corrections on multiple images. Moreover, the feasibility of bundle adjustment processing using various satellite image data was confirmed. As a result, it is expected that a large volume of satellite images processed quickly and accurately will be provided for various satellite image application fields. Copyright © 2024 Korean Society of Remote Sensing.

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