Abstract

Line scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a scene. These data can be useful for detecting sub-pixel targets. Such systems, however, may be limited by certain key characteristics in their design. Systems employing multiple spectrometers, or that collect data from multiple focal planes may suffer an inherent misregistration between sets of collected spectral bands. In order to utilize the full spectrum for target detection purposes, the sets of bands must be registered to each other as precisely as possible. Perfect registration is not possible, due to both the sensor design, and variation in sensor orientation during data acquisition. The issue can cause degradation in the performance of various target detection algorithms. An analysis of algorithms is necessary to determine which perform well when working with misregistered data. In addition, new algorithms may need to be developed which are more robust in these conditions. The work set forth in this thesis will improve the registration between spectral bands in a line scanning hyperspectral sensor by using a geometric model of the sensor along with aircraft orientation parameters to pair sets of image pixels based on their ground locations. Synthetic scenes were created and band-to-band misregistration was induced between the VIS and NIR spectral channels to test the performance of various hyperspectral target detection algorithms when applied to misregistered hyperspectral data. The results for this case studied show geometric algorithms perform well using only the VIS portion of the EM spectrum, and do not always benefit from the addition of NIR bands, even for small amounts of misregistration. Stochastic algorithms appear to be more robust than geometric algorithms for datasets with band-to-band misregistration. The stochastic algorithms tested often benefit from the addition of NIR bands, even for large amounts of misregistration.

Library of Congress Subject Headings

Remote sensing--Data processing--Evaluation; Remote sensing--Mathematics; Image processing--Digital techniques; Computer algorithms--Evaluation

Publication Date

5-2008

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

John P. Kerekes

Advisor/Committee Member

Emmett J Ientilucci

Advisor/Committee Member

Harvey E Rhody

Comments

Physical copy available from RIT's Wallace Library at TA1637 .C37 2008

Campus

RIT – Main Campus

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