Abstract

Since the development of spectral imaging systems where we transitioned from panchromatic, single band images to multiple bands, we

have pursued a way to evaluate the quality of spectral images. As spectral imaging capabilities improved and the bands collected wavelengths outside of the visible spectrum they could be used to gain information about the earth such as material identification that would have been a challenge with panchromatic images. We now have imaging systems capable of collecting images with hundreds of contiguous bands across the reflective portion of the electromagnetic spectrum that allows us to extract information at subpixel levels. Prediction and assessment methods for panchromatic image quality, while well-established are continuing to be improved. For spectral images however, methods for analyzing quality and what this entails have yet to form a solid framework.

In this research, we built on previous work to develop a process to optimize the design of spectral imaging systems. We used methods for predicting quality of spectral images and extended the existing framework for analyzing efficacy of miniature systems. We comprehensively analyzed utility of spectral images and efficacy of compact systems for a set of application scenarios designed to test the relationships of system parameters, figures of merit, and mission requirements in the trade space for spectral images collected by a compact imaging system from design to operation. We focused on subpixel target detection to analyze spectral image quality of compact spaceborne systems with adaptive band selection capabilities.

In order to adequately account for the operational aspect of exploiting adaptive band collection capabilities, we developed a method for

band selection. Dimension reduction is a step often employed in processing spectral images, not only to improve computation time but to avoid errors associated with high dimensionality. An adaptive system with a tunable filter can select which bands to collect for each target so the dimension reduction happens at the collection stage instead of the processing stage. We developed the band selection method to optimize detection probability using only the target reflectance signature. This method was conceived to be simple enough to be calculated by a small on-board CPU, to be able to drive collection decisions, and reduce data processing requirements. We predicted the utility of the selected bands using this method, then validated the results using real images, and cross-validated them using simulated image associated with perfect truth data. In this way, we simultaneously validated the band selection method we developed and the combined use of the simulation and prediction tools used as part of the analytic process to optimize system design.

We selected a small set of mission scenarios and demonstrated the use of this process to provide example recommendations for efficacy and utility based on the mission. The key parameters we analyzed to drive the design recommendations were target abundance, noise, number of bands, and scene complexity. We found critical points in the system design trade space, and coupled with operational requirements, formed a set of mission feasibility and system design recommendations. The selected scenarios demonstrated the relationship between the imaging system design and operational requirements based on the mission. We found key points in the spectral imaging trade space that indicated relationships within the spectral image utility trade space that can be used to further solidify the frameworks for compact spectral imaging systems.

Library of Congress Subject Headings

Spectral imaging; Imaging systems--Image quality

Publication Date

7-11-2019

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

John Kerekes

Advisor/Committee Member

David Ross

Advisor/Committee Member

Emmett Ientilucci

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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