Hybridization of hyperspectral imaging target detection algorithm chains

David C. Grimm, David W. Messinger, John P. Kerekes, John R. Schott, Sylvia S. Shen, Paul E. Lewis
2005 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI  
Public reporting burden for ttiis collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of Information, Including suggestions for reducing this burden, to ABSTRACT Detection of a known target in an image has several
more » ... 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 a particular image or target type. Several different algorithms for each step will be presented, to include ELM, FLAASH, MNF, PPI, MAXD, the structured background matched filters OSP, and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, receiver operating characteristic (ROC) curves will be calculated for each algorithm chain and, as an end result, a comparison of the various algorithm chains will be presented.
doi:10.1117/12.605889 fatcat:wf5mo2qdtjdplkudvpatbkmt2a