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Second Order Statistics Targets-Spec...

Paylor, Andrew.

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Second Order Statistics Targets-Specified Virtual Dimensionality.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Second Order Statistics Targets-Specified Virtual Dimensionality./
作者:	Paylor, Andrew.
面頁冊數:	180 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
標題:	Engineering, General. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3624395
ISBN:	9781303977121

Second Order Statistics Targets-Specified Virtual Dimensionality.
Paylor, Andrew.

Second Order Statistics Targets-Specified Virtual Dimensionality. - 180 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--University of Maryland, Baltimore County, 2014.

Hyperspectral imaging has emerged as a productive and useful technique in remote sensing. With its high spectral resolution the materials in a scene can be detected, discriminated, and identified. This has come at the cost of challenges in the storage, transmission, and processing of the data to retrieve the information of interest. Since the number of spectral bands in a datacube is typically much larger than the true dimensionality of the data approaches have been sought to reduce the dimensionality of the data to allieviate the processing requirements. To enable this effectively it is necessary to know the dimensionality of this subspace.

ISBN: 9781303977121Subjects--Topical Terms:

1020744
Engineering, General.

Second Order Statistics Targets-Specified Virtual Dimensionality.
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245 1 0 $a Second Order Statistics Targets-Specified Virtual Dimensionality.
300 $a 180 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Adviser: Chein-I Chang.
502 $a Thesis (Ph.D.)--University of Maryland, Baltimore County, 2014.
520 $a Hyperspectral imaging has emerged as a productive and useful technique in remote sensing. With its high spectral resolution the materials in a scene can be detected, discriminated, and identified. This has come at the cost of challenges in the storage, transmission, and processing of the data to retrieve the information of interest. Since the number of spectral bands in a datacube is typically much larger than the true dimensionality of the data approaches have been sought to reduce the dimensionality of the data to allieviate the processing requirements. To enable this effectively it is necessary to know the dimensionality of this subspace.
520 $a The primary focus of this dissertation is the development of unsupervised approaches to determine this subspace. In this work the focus is on using least-squares techniques to identify the dimensionality and subspace basis. We are specifically interested in a target-specified approach, that is in identifying a subspace of minimum dimension whose basis vectors are actual signatures present in the image. To distinguish this from a subspace basis of pure signatures we call these basis vectors virtual endmembers.
520 $a This work develops a theory of second-order statistics target-specified virtual dimensionality. Virtual dimensionality is defined as the number of spectrally distinct signatures in hyperspectral data. Unfortunately there is no universal definition of "spectrally distinct." In developing the theory of second-order statistics targets-specified virtual dimensionality spectrally distinct is defined in the second-order statistics sense, thus the number of virtual endmember basis vector is the appropriate virtual dimensionality.
590 $a School code: 0434.
650 4 $a Engineering, General. $3 1020744
650 4 $a Statistics. $3 517247
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710 2 $a University of Maryland, Baltimore County. $b Engineering, Electrical. $3 1057914
773 0 $t Dissertation Abstracts International $g 75-10B(E).
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793 $a English
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