Abstract

In the realm of hyperspectral (HS) sub-pixel target detection, the Linear Mixing Model (LMM) proposes that the macroscopic interaction between incident light and materials in a scene may be modeled as linear. For individual pixels in hyperspectral data which contain multiple materials, this means that they may be accurately represented by a linear combination of the spectra of those pure materials, which are often called endmembers. However, when nonlinear mixing, such as shadowing and adjacent reflections, are present, the foundational assumptions of the LMM are violated and its accuracy in predicting target detection performance is reduced. This thesis aims to investigate the impact of such nonlinear effects on the performance of the LMM with regards to a HS sub-pixel target detection task. To quantify the impact of nonlinear effects, an experimental data collect was completed in September 2022 with a Headwall Nano HSI sensor at RIT’s Tait Preserve. The Headwall Nano has 272 bands in the visible to the near-infrared (VNIR) region of the electromagnetic spectrum. This data collect utilized five novel sub-pixel targets designed by Chase Canas with pre-determined fill fractions of 100%, 80%, 60%, 40%, and 20%. In addition to the collection of experimental data, two types of modeling software were leveraged to produce results that were based on the LMM and that utilized a path-tracing technique which is better able to deal with nonlinear effects. The Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model was designed to predict HS sub-pixel target detection performance. The LMM is foundational to FASSP’s systems’ performance computations and is used by FASSP to model the propagation of spectral radiance from a user-specified scene through a MODTRAN-informed atmosphere to a user-defined sensor. FASSP relies on a vast array of user inputs to specify the sensor parameters and post-processing algorithms used to detect a target spectrum. FASSP also includes parameters that can account for the shadowing of the target class. As FASSP has been validated in previous studies, it is a reliable reference that can be used to simulate the performance of the LMM both when nonlinear effects are and aren’t present. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model was first developed in the 1990s by the Digital Imaging and Remote Sensing (DIRS) lab at the Chester F. Carlson Center for Imaging Science (CIS) at the Rochester Institute of Technology (RIT). DIRSIG utilizes a path-tracing model and the three-dimensional geometry of a scene and the spectral properties of the materials within to generate radiometrically-accurate synthetic data that is able to capture nonlinear effects. As a result of the differences in their modeling paradigms, FASSP and DIRSIG provide two different perspectives on a systems’ performance analysis of a HS sub-pixel target detection task. Four setups were designed for the experimental collect to investigate the effects of shadowing and non-linear mixing on the performance of the LMM. These four setups were replicated within FASSP and DIRSIG. Five targets with varying fill fraction percentages were painted a distinct color and served as the target class within the scene. To replicate the effects of shadowing, five panels were constructed and placed to shadow the five targets. To induce multiple reflections and spectral contamination, five treeshine reflector (TR) panels were constructed and painted with a bright red paint. To determine the effectiveness of this contamination and to inform specular material descriptions in DIRSIG, the Bi-Directional Reflectance Factor (BRF) of the TR panels was also measured with RIT’s Goniometer at the Rochester Institute of Technology version Two (GRIT-T) instrument. The four setups consisted of a base setup with no shadowing or TR panels, a shadowing panel setup, a TR-S panel setup with both the shadowing and TR panels, and a TR panel setup. Results were produced in the form of predicted mean target radiances, Signal-to-Noise-Ratios (SNRs) and target detection performances in the form of Receiver-Operating Characteristic (ROC) curves, and Area-Under-the-Curves (AUCs). By leveraging the three sources of data (experimental, the LMM-based FASSP, and the path-tracing DIRSIG), a unique parallel analysis was performed to determine the ability of each modeling approach to deal with shadowing and adjacent reflections. It was found that FASSP’s ability to accurately predict mean target radiances, SNRs, and target detection performances was degraded dramatically relative to DIRSIG when shadowing was present. Its accuracy was also degraded when adjacent reflections were present, but not to the same degree. Results were also produced to examine the impact of including a description of the TR panel specularity in the DIRSIG simulations; it was found that utilizing a Lambertian TR panel resulted in higher mean target radiances than a specular one, but only when the shadowing was also present. The results from DIRSIG indicated a high level of accuracy, however, more work could be conducted to improve the user inputs to ensure optimal settings.

Library of Congress Subject Headings

Hyperspectral imaging--Data processing; Linear models (Statistics)--Evaluation; Remote sensing--Data processing

Publication Date

9-18-2023

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science

College

College of Science

Advisor

John Kerekes

Advisor/Committee Member

Emmett Ientilucci

Advisor/Committee Member

Jan van Aardt

Campus

RIT – Main Campus

Plan Codes

IMGS-MS

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