Physics-based Detection of Subpixel Targets in Hyperspectral Imagery
University of Maryland College Park United States
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Hyperspectral imagery provides the ability to detect targets that are smaller than the size of a pixel. They provide this ability by measuring the reflection and absorption of light at different wave lengths creating a spectral signature for each pixel in the image. This spectral signature contains information about the different materials within the pixel therefore, the challenge in subpixel target detection lies in separating the targets spectral signature from competing background signatures. Most research has approached this problem in a purely statistical manner. Our approach fuses statistical signal processing techniques with the physics of reflectance spectroscopy and radiative transfer theory. Using this approach, we provide novel algorithms for all aspects of subpixel detection from parameter estimation to threshold determination. Characterization of the target and background spectral signatures is a key part of subpixel detection. We develop an algorithm to generate target signatures based onradiative transfer theory using only the image and a reference signature without the need for calibration, weather information, or source-target-receiver geometries. For background signatures, our work identifies that even slight estimation errors in the number of background signatures can severely degrade detection performance. To this end, we present a new method to estimate the number of background signatures specifically for subpixel target detection. At the core of the dissertation is the development of two hybrid detectors which fuse spectroscopy with statistical hypothesis testing. Our results show that the hybrid detectors provide improved performance in three different ways insensitivity to the number of background signatures, improved detection performance, and consistent performance across multiple images leading to improved receiver operating characteristic curves. Lastly, we present a novel adaptive threshold estimate via extreme value theory.