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An Automated Directed Spectral Search Methodology for Small Target Detection

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dc.contributor.advisor Stefanidis, Tony
dc.contributor.author Grossman, Stanley I.
dc.creator Grossman, Stanley I. en_US
dc.date.accessioned 2014-09-18T01:56:08Z
dc.date.available 2014-09-18T01:56:08Z
dc.date.issued 2014-05 en_US
dc.identifier.uri https://hdl.handle.net/1920/8885
dc.description.abstract Much of the current efforts in remote sensing tackle macro-level problems such as determining the extent of wheat in a field, the general health of vegetation or the extent of mineral deposits in an area. However, for many of the remaining remote sensing challenges being studied currently, such as border protection, drug smuggling, treaty verification, and the war on terror, most targets are very small in nature - a vehicle or even a person. While in typical macro-level problems the objective vegetation is in the scene, for small target detection problems it is not usually known if the desired small target even exists in the scene, never mind finding it in abundance. The ability to find specific small targets, such as vehicles, typifies this problem. Complicating the analyst's life, the growing number of available sensors is generating mountains of imagery outstripping the analysts' ability to visually peruse them.
dc.format.extent 191 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2014 Stanley I. Grossman en_US
dc.subject Remote sensing en_US
dc.subject analytic sweet spot en_US
dc.subject automated target detection en_US
dc.subject directed search en_US
dc.subject nearest neighbor inflation en_US
dc.subject spectral searach en_US
dc.title An Automated Directed Spectral Search Methodology for Small Target Detection en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Earth Systems and Geoinformation Sciences en
thesis.degree.grantor George Mason University en


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