当前位置: X-MOL 学术GISci. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Estimation and improvement in the geolocation accuracy of rational polynomial coefficients with minimum GCPs using KOMPSAT-3A
GIScience & Remote Sensing ( IF 6.7 ) Pub Date : 2020-07-12 , DOI: 10.1080/15481603.2020.1791499
Namhoon Kim 1 , Yoonjo Choi 1 , Junsu Bae 1 , Hong-Gyoo Sohn 1
Affiliation  

ABSTRACT In this paper, we propose a method to regenerate Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery and to reduce the geolocation error using minimum ground control points (GCPs). To estimate the new RPCs, the physical sensor model fitted to KOMPSAT-3A imagery was utilized and virtual GCPs over the study area were created. The size of the virtual grid used was 20x20x20. To remove the sensor-related errors in physical sensor model, three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were additionally applied. We evaluated our proposed method in two areas within Korea, one in urban (Seoul) and one in rural (Goheung) areas. The results showed that there was a significant improvement after applying the suggested approach in the two areas. The image coordinate translation model is suggested in terms of GCP requirement and expected errors estimated from the error propagation analysis using Gauss–Markov Model (GMM).

中文翻译:

使用 KOMPSAT-3A 估计和改进具有最小 GCP 的有理多项式系数的地理定位精度

摘要 在本文中,我们提出了一种使用 KOMPSAT-3A 图像重新生成有理多项式系数 (RPC) 并使用最小地面控制点 (GCP) 减少地理定位误差的方法。为了估计新的 RPC,使用了适合 KOMPSAT-3A 图像的物理传感器模型,并在研究区域上创建了虚拟 GCP。使用的虚拟网格的大小为 20x20x20。为了消除物理传感器模型中与传感器相关的误差,另外应用了三种不同的图像校正模型(图像坐标平移模型、偏移和漂移模型以及仿射变换模型)。我们在韩国的两个地区评估了我们提出的方法,一个在城市(首尔),一个在农村(高兴)地区。结果表明,在这两个领域应用所建议的方法后都有显着的改进。
更新日期:2020-07-12
down
wechat
bug