Abstract

The detection and characterization of powdered contaminants is a challenging aspect of remote sensing in the longwave infrared spectral region. Powders are small in size (less than 45 microns in diameter) and exhibit weakened spectral features due to increased volume scattering, which is more prevalent as particle size decreases. Meanwhile, atmospheric effects such as wind, clouds, or shadowing cause large fluctuations in temperature of a surface on a microscale. This affects the ability of temperature emissivity separation algorithms to adequately derive a material’s spectral emissivity from spectral radiance measurements. Hazardous powdered contaminants that are inaccessible need to be monitored from afar by using instruments of remote sensing. While spectral emissivity signatures alone can be useful, information about the physical properties and phenomenology of the material would be advantageous. Therefore, a method for estimating various physical properties including contaminant mass is presented. The proposed method relies on the principles of the Non-Conventional Exploitation Factors Data System (NEFDS) Contamination Model, which creates spectral reflectance mixtures based on two materials from its own database. Here, a three-step parameter inversion model was utilized that estimates several physical parameters to derive a contaminant mass from three spectral emissivity measurements. This information is then used to inject synthetic target mixtures into real airborne hyperspectral imagery from the Blue Heron LWIR sensor at pixel and sub-pixel levels. Target detection was performed on these images using the adaptive cosine/coherence estimator (ACE) with several types of target spectra. The target spectrum with the largest detection statistic for each target pixel represents the best spectrum to detect its physical properties and informs the detection method. Results indicate that best performance is not always achieved when using the pure contaminant spectrum, but varies with level of contamination and pixel fill fraction. The inversion algorithm method was also applied to real targets in LWIR imagery and demonstrated the ability to extract contaminant mass from the data directly.

Publication Date

9-21-2017

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

David W. Messinger

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Charles M. Bachmann

Campus

RIT – Main Campus

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