Publication Date

2018

Document Type

Thesis

Committee Members

Joshua Ash (Advisor); Fred Garber (Committee Member); Arnab Shaw (Committee Member)

Degree Name

Master of Science in Electrical Engineering (MSEE)

Abstract

In this thesis, we develop and evaluate change detection algorithms for longwave infrared (LWIR) hyperspectral imagery. Because measured radiance in the LWIR domain depends on unknown surface temperature, care must be taken to prevent false alarms resulting from in-scene temperature differences that appear as material changes. We consider fewer variables. Examples using synthetic and measured data quantify the computational complexity of the proposed methods and demonstrate orders of magnitude reduction in false alarm rates relative to existing methods. four strategies to mitigate this effect. In the first, pre-processing via traditional temperatureemissivity separation yields approximately temperature-invariant emissivity vectors for use in change detection. In the second, we utilize alpha residuals to obtain robustness to temperature errors. Next, we adopt a minimax approach that minimizes the maximal spectral deviation between measurements. Finally, we reduce our minmax approach to solve with

Page Count

97

Department or Program

Department of Electrical Engineering

Year Degree Awarded

2018

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

ORCID ID

0000-0003-2929-2206


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