Detection of a known target in an image has several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different steps involved, some have a more significant impact than others on the final result the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented.

This research seeks to identify the most effective set of algorithms for detecting a known tar get. Several different algorithms for each step will be presented, to include ELM, FLAASH, ACORN, MNF, PPI, N-FINDR, MAXD, and two matched filters that employ a structured background model OSP and ASD. The chains generated by these algorithms will be com pared using the Forest Radiance I HYDICE data set. Finally, ROC curves and AFAR values are calculated for each algorithm chain and a comparison of them is presented. Detection rates at a CFAR are also compared. Since a relatively small number of algorithms were used for each step, there were no definitive results generated. However, a comprehensive comparison of the chains using the above mentioned algorithms is presented.

Library of Congress Subject Headings

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

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


John Schott

Advisor/Committee Member

John Kerekes

Advisor/Committee Member

David Messinger


Physical copy available from RIT's Wallace Library at TA1637 .G74 2005


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