Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery

dc.contributor.advisor Mersereau, Russell M.
dc.contributor.author Farrell, Michael D., Jr. en_US
dc.contributor.committeeMember Copeland, John A.
dc.contributor.committeeMember Cunnold, Derek
dc.contributor.committeeMember Lanterman, Aaron D.
dc.contributor.committeeMember McClellan, James H.
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2006-01-18T22:17:26Z
dc.date.available 2006-01-18T22:17:26Z
dc.date.issued 2005-11-03 en_US
dc.description.abstract Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken. This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics. First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC-t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance. In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 5225436 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/7505
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Covariance contamination en_US
dc.subject Dimension reduction
dc.subject Hyperspectral imaging
dc.subject Image processing Digital techniques
dc.subject Land mines
dc.subject Remote sensing
dc.subject Target detection
dc.title Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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