This thesis performs a performance comparison on existing hyperspectral target detection algorithms. The algorithms chosen for this analysis include multiple adaptive matched filters and the physics based modeling invariant technique. The adaptive matched filter algorithms can be divided into either structured (geometrical) or unstructured (statistical) algorithms. The difference between these two categories is in the manner in which the background is characterized. The target detection procedure includes multiple pre-processing steps that are examined here as well. The effects of atmospheric compensation, dimensionality reduction, background characterization, and target subspace creation are all analyzed in terms of target detection performance. At each step of the process, techniques were chosen that consistently improved target detection performance. The best case scenario for each algorithm is used in the final comparison of performance. The results for multiple targets were computed and statistical matched filter algorithms were shown to outperform all others in a fair comparison. This fair comparison utilized a FLAASH atmospheric compensation for the matched filters that was equivalent to the physics based invariant process. The invariant technique was shown to outperform the geometric matched filters that it uses in its approach. Each of these techniques showed improvement over the SAM algorithm for three of the four targets analyzed. Multiple theories are proposed to explain the anomalous results for the most difficult target.
Library of Congress Subject Headings
Remote sensing--Data processing--Evaluation; Image processing--Digital techniques; Remote sensing--Mathematics; Remote sensing--Atmospheric effects; Computer algorithms--Evaluation
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Schott, John - Chair
Cisz, Adam, "Performance comparison of hyperspectral target detection algorithms" (2006). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus