Linear Mixing Model Performance with Nonlinear Effects in Hyperspectral Sub-Pixel Target Detection

In the realm of hyperspectral sub-pixel target detection, the Linear Mixing Model (LMM) is an established basis for analysis and modelling. However, its accuracy depends on several key assumptions, most notably that there exists no nonlinear mixing within a scene. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model utilizes the LMM to perform system requirement analyses. To quantify the limitations of the LMM when nonlinear effects are present, this paper reviews the results of September 2022 data collect in which the spectra of several sub-pixel target panels were altered by shadowing and reflectance panels and compares it with those from FASSP. Overall, both forms of nonlinear effects reduce target detection performance and the FASSP model tends to overestimate target radiance and target detection performance relative to the empirical results when nonlinear effects are present.