Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm
| dc.contributor.author | Schwickerath, Anthony N., author | |
| dc.contributor.author | Kirby, Michael, advisor | |
| dc.contributor.author | Peterson, Christopher, committee member | |
| dc.contributor.author | Anderson, Charles, committee member | |
| dc.date.accessioned | 2007-01-03T08:11:12Z | |
| dc.date.available | 2007-01-03T08:11:12Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | There is currently interest in detecting the use of chemical and biological weapons using hyperspectral sensors. Much of the research in this area assumes the spectral signature of the weapon is known in advance. Unfortunately, this may not always be the case. To obviate the reliance on a library of known target signatures, we instead view this as an anomaly detection problem. In this thesis, the RX algorithm, a benchmark anomaly detection algorithm for multi- and hyper-spectral data is reviewed, as are some standard extensions. This class of likelihood ratio test-based algorithms is generally applied to aerial imagery for the identification of man-made artifacts. As such, the model assumes that the scale is relatively consistent and that the targets (roads, cars) also have fixed sizes. We apply these methods to terrestrial video of biological and chemical aerosol plumes, where the background scale and target size both vary, and compare preliminary results. To explore the impact of parameter choice on algorithm performance, we also present an empirical study of the standard RX algorithm applied to synthetic targets of varying sizes over a range of settings. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Schwickerath_colostate_0053N_11324.pdf | |
| dc.identifier | ETDF2012500249MATH | |
| dc.identifier.uri | http://hdl.handle.net/10217/68152 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.title | Anomaly detection in terrestrial hyperspectral video using variants of the RX algorithm | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Mathematics | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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