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Replacement Sensor Model Generation for a Long High-Resolution Satellite Image Strip

机译:为长高分辨率卫星图像条生成替换传感器模型

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摘要

Many high-resolution satellite images are acquired in the strip mode, in which the imaging direction is aligned with the satellite trajectory. A long strip image is often sliced into several scenes before delivery, and customers preprocess each scene with auxiliary data before their applications. Sensor model information is often provided for each scene in the form of rational polynomial coefficients (RPCs). The original RPCs of each scene are erroneous, and should be bias-compensated using ground reference data or ground control points. In this study, we propose the use of single RPCs for long image strips. However, a simple replacement of the physical sensor model using RPCs for the entire strip may cause large errors, because RPCs do not model large satellite attitude changes. Therefore, we applied the fitting of the satellite attitude to a linear equation before RPCs generation for a long-strip image. The test was conducted for three Kompsat-3A image strips consisting of multiple scenes, where one strip shows large satellite attitude changes. From the experiments, the RPCs generated for the whole image strip showed large errors compared to the strip adjustment; however, the proposed method could reduce the errors to an accuracy comparable to that of scene-based RPCs.
机译:许多高分辨率卫星图像都是在带状模式下获取的,其中成像方向与卫星轨迹对齐。长条形图像通常在交付前被切成几个场景,客户在应用之前使用辅助数据对每个场景进行预处理。传感器模型信息通常以有理多项式系数 (RPC) 的形式提供给每个场景。每个场景的原始RPC都是错误的,应使用地面参考数据或地面控制点进行偏置补偿。在这项研究中,我们建议对长图像条使用单个RPC。但是,对整个条带使用 RPC 简单地替换物理传感器模型可能会导致较大的误差,因为 RPC 不会对大型卫星姿态变化进行建模。因此,在RPC生成之前,我们将卫星姿态拟合应用于线性方程,以获得长带图像。该测试是针对由多个场景组成的三个Kompsat-3A图像条进行的,其中一个条带显示较大的卫星姿态变化。从实验中可以看出,与条带平差相比,为整个图像条带生成的RPC显示出较大的误差;然而,所提出的方法可以将误差降低到与基于场景的RPC相当的精度。

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