Title

Hyperspectral target detection performance modeling

Date of Award

2015

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Russell C. Hardie

Abstract

Hyperspectral remote sensing has become a popular topic of research due to the numerous applications stemming from the high dimensionality of the recorded spectral data. From the design perspective, hyperspectral sensors are generally more complex than standard color or infrared imaging systems because there are more optical components in the system. The quality of each of these components directly affects the target detection performance of the system. In addition to the integrity of optical components, target detection performance is also affected by signal variations due to sensor noise. This research addresses the design of an end-to-end hyperspectral imaging system performance model that incorporates the optical design of the system as well as the stochastic nature of data collected by electronic remote sensing. A system transmission model is presented that calculates the camera signal as a function of input radiance and accounts for each individual optical element in the imaging system. This model can be used to analyze the performance sensitivities of a specific component for a variety of target detection scenarios. The accuracy of the system transmission model is assessed using calibrated hyperspectral data. In addition to the system transmission model, a realistic statistical data model is proposed. Many data models currently account for sensor noise with an additive, stationary variance. This research expands upon this by implementing an additive, signal-dependent sensor noise model that more accurately represents the true phenomena driving the sensor noise. The same data set is used to test target detection performance using the signal-dependent noise model. The results are analyzed to investigate the possible benefits of using the proposed noise model. The data used for this research was collected at Wright Patterson Air Force Base 25-26 June 2014. The scene consists of a grassy background with eight painted wooden panel targets. Data collections took place at different times of day in order to capture varying solar angles and illumination levels. Additionally, data was collected with varying exposure times in an effort to observe performance effects due to varying signal-to-noise ratios. Conclusions about the performance of the system transmission and data modeling techniques are framed within the context of collection time and exposure time.

Keywords

Optical detectors Simulation methods, Spectral imaging Simulation methods, Multispectral imaging Simulation methods, Remote sensing, Electrical Engineering, Remote Sensing, Statistics, Hyperspectral, Target Detection, Performance Modeling

Rights Statement

Copyright 2015, author

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