With the popularity of (hyperspectral) remote sensing systems coupled with a myriad of applications comes the need for investigations into hyperspectral system designs and parameter trade-off studies. Analytical models based on statistical descriptions and signal propagation are efficient methods to examine these parameter trade-off studies, as well as sensitivity studies, with low computational cost. In this article, a newly developed long-wave infrared (LWIR) statistical iterative spectrally smooth temperature/emissivity separation (S-ISSTES) algorithm has been integrated into a widely used full-spectrum hyperspectral remote sensing system model known as the forecasting and analysis of spectroradiometric system performance (FASSP) model. This new tool now allows users to perform LWIR full system (i.e., from surface reflectance, to sensor, to retrieve emissivity, to detection analysis) trade studies. In this article, we validate the LWIR model and detection performance of the new FASSP model followed by illustrating the usage of the full-system model by demonstrating trade examples including useful parameter trade studies and subpixel detection sensitivity studies.

中文翻译：

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具有 LWIR TES 功能的全光谱光谱成像系统分析模型


随着（高光谱）遥感系统的普及以及无数应用的出现，需要对高光谱系统设计和参数权衡研究进行调查。基于统计描述和信号传播的分析模型是检查这些参数权衡研究以及敏感性研究的有效方法，且计算成本低。在本文中，新开发的长波红外（LWIR）统计迭代光谱平滑温度/发射率分离（S-ISSTES）算法已集成到广泛使用的全光谱高光谱遥感系统模型中，称为预测和分析光谱辐射系统性能（FASSP）模型。这个新工具现在允许用户执行长波红外完整系统（即从表面反射率到传感器，检索发射率，到检测分析）贸易研究。在本文中，我们验证了 LWIR 模型和新 FASSP 模型的检测性能，然后通过演示贸易示例（包括有用的参数贸易研究和亚像素检测灵敏度研究）来说明全系统模型的用法。