The Rational Polynomial Camera (RPC) model can be used to describe a variety of image acquisition systems in remote sensing, notably optical and Synthetic Aperture Radar (SAR) sensors. RPC functions relate 3D to 2D coordinates and vice versa, regardless of physical sensor specificities, which has made them an essential tool to harness satellite images in a generic way. This article describes a terrain-independent algorithm to accurately derive a RPC model from a set of 3D-2D point correspondences based on a regularized least squares fit. The performance of the method is assessed by varying the point correspondences and the size of the area that they cover. We test the algorithm on SAR and optical data, to derive RPCs from physical sensor models or from other RPC models after composition with corrective functions.
翻译:理性聚合照相机(RPC)模型可用于描述遥感中的各种图像采集系统,特别是光学和合成孔径雷达传感器(SAR)传感器,RPC函数将3D与2D坐标联系起来,反之亦然,而不论其物理传感器特性如何,使RPC函数成为以通用方式利用卫星图像的基本工具。本文章描述了一种地形独立的算法,以精确地从一套基于固定最小方位的3D-2D点通信中得出RPC模型。该方法的性能是通过不同点通信和其所覆盖区域的大小来评估的。我们对SAR和光学数据的算法进行测试,以便从物理传感器模型或具有矫正功能的其他RPC模型中得出RPC模型。