论文标题
端到端模拟的统计学习
Statistical Learning for End-to-End Simulations
论文作者
论文摘要
端到端任务绩效模拟器（E2ES）是加速卫星任务开发的合适工具。这些E2ES的一个核心要素是地球观察任务的各种工具所观察到的合成场景的产生。这些场景的产生依赖于辐射转移模型（RTM）来模拟与地面和大气层的光相互作用。但是，由于其巨大的计算负担，高级RTM的执行是不切实际的。因此，经典的插值和统计仿真方法的预计查找表（LUT）是在合理时间内生成合成场景的常见实践。这项工作评估了插值和仿真方法的准确性和计算成本，以采样输入LUT可变空间。基于Mondtran的大气顶辐射数据的结果表明，高斯工艺模拟器在其时间的一部分时间内产生了比线性插值更准确的输出光谱。可以得出结论，仿真可以作为LUT参数空间采样的快速，更准确的替代方案。
End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the generation of synthetic scenes that are observed by the various instruments of an Earth Observation mission. The generation of these scenes rely on Radiative Transfer Models (RTM) for the simulation of light interaction with the Earth surface and atmosphere. However, the execution of advanced RTMs is impractical due to their large computation burden. Classical interpolation and statistical emulation methods of pre-computed Look-Up Tables (LUT) are therefore common practice to generate synthetic scenes in a reasonable time. This work evaluates the accuracy and computation cost of interpolation and emulation methods to sample the input LUT variable space. The results on MONDTRAN-based top-of-atmosphere radiance data show that Gaussian Process emulators produced more accurate output spectra than linear interpolation at a fraction of its time. It is concluded that emulation can function as a fast and more accurate alternative to interpolation for LUT parameter space sampling.
