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ECE MS Thesis Defense: Owen McElhinney
August 4, 2021 @ 10:00 am - 11:00 am
MS Thesis Defense: On the Application of Spline Functions to Problems in Hyperspectral Imaging
Location: Zoom Link
Abstract: Hyperspectral Imaging (HSI) is a rapidly growing topic in the field of remote sensing. Hyperspectral cameras trade off a reduction in the spatial resolution of modern imaging for a higher spectral resolution. This allows for the detection of surface materials using the principles of spectroscopy.
This work will investigate the application of a class of functions called splines to three different problems in the field of HSI. Splines are a special class of data-fitting functions that guarantee continuity. Common data-fitting techniques like polynomial and piecewise-polynomial fitting are unable to match the complexity of HSI data. Splines provide a robust fitting procedure that matches the physical reality of material spectra. They can additionally be used to smooth data, calculate derivatives, interpolate between points, and more.
The first problem will look at the smoothing of noisy data for detecting small materials. When objects are smaller than the pixel, the observed spectrum will mix the target with background materials. The problem of unmixing removes the influence of these background materials from observed data. If the object is too small, the estimates from unmixing will be dominated by error. In this scenario, splines will be used to smooth out these random variations.
The second problem will use splines to introduce a new solution to the problem of Temperature Emissivity Separation (TES). The physical quantity captured by the camera is radiance. For the detection of materials, ground reflectance or emissivity are desired. TES is the process by which ground radiance is converted to material emissivity. Splines will be used to replace estimated roughness in this problem with an analytical solution.
The third and final application looks at using splines to detect gases without relying on image statistics. Gas features are sharp and only impact a narrow window of the spectrum. This application attempted to use splines to detect these sharp features by looking at the difference between the collected data and an interpolation across the feature.
The first two applications yielded interesting and useful results. The third application yielded some interesting conclusions about the general problem and improved methods for using splines in this space.