Imaging Science MS Thesis Defense: Colin Maloney
MS Thesis Defense
A Study on the Impact of Nonlinear Effects on Hyperspectral Sub-Pixel Target Detection
Colin Maloney
Imaging Science MS Candidate
Chester F. Carlson Center for Imaging Science, RIT
Abstract: 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. In contrast, Digital Imaging and Remote Sensing Image Generation (DIRSIG) uses a path-tracing technique that is capable of capturing both the linear and nonlinear effects in a scene to generate radiometrically-accurate data. To quantify the limitations of the LMM when these nonlinear effects are present, we review the results of a September 2022 data collect in which controlled nonlinear effects were imposed on a sub-pixel target detection task and the corresponding results from both FASSP and DIRSIG. Overall, the presence of shadowing and multiple reflections resulted in altered target radiances and reduced target detection performance with the data collect and our two types of software. In addition, our LMM-based model, FASSP, possess reduced accuracy when these nonlinear effects are present; DIRSIG appears to better consider the impact of these nonlinear effects.
Intended Audience: Undergraduates, graduates, and experts. Those with interest in the topic.
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