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Subpixel detection of 3D objects in ...

Liu, Yong.

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Subpixel detection of 3D objects in hyperspectral imagery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Subpixel detection of 3D objects in hyperspectral imagery./
作者:	Liu, Yong.
面頁冊數:	73 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1190.
Contained By:	Dissertation Abstracts International68-02B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3252672

Subpixel detection of 3D objects in hyperspectral imagery.
Liu, Yong.

Subpixel detection of 3D objects in hyperspectral imagery. - 73 p.

Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1190.

Thesis (Ph.D.)--University of California, Irvine, 2007.

The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The effective use of this information is crucial for a detection algorithm to achieve high accuracy under challenging conditions.Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Subpixel detection of 3D objects in hyperspectral imagery.
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245 1 0 $a Subpixel detection of 3D objects in hyperspectral imagery.
300 $a 73 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-02, Section: B, page: 1190.
500 $a Adviser: Glenn Healey.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2007.
520 $a The large amount of spectral information in hyperspectral imagery allows the accurate detection of subpixel objects. The effective use of this information is crucial for a detection algorithm to achieve high accuracy under challenging conditions.
520 $a The accuracy of subpixel detection degrades with approximation errors of subspace representations arising from cluttered backgrounds and complex target objects. We develop a non-parametric generalized likelihood ratio (NGLR) statistic for the subpixel detection of 3-D objects that is invariant to the illumination and atmospheric conditions. We construct the target and background subspaces from target models and the image data. The NGLR is established by nonparametrically estimating the conditional probability densities for the background and target hypotheses using subspace residuals.
520 $a The discriminant achieved by subspace representations plays a critical role in the accurate detection. We establish subspace representations by means of linear discriminatory analysis for 3D objects and backgrounds to improve discriminability for 3D detection invariant to unknown illumination and atmospheric conditions. Residual variance information is utilized to generate background and mixed residual statistics which improve the separation of target and background for detection. A new detection algorithm that uses these statistics in conjunction with a likelihood ratio test is proposed for the subpixel detection of complex 3D objects in cluttered backgrounds. Other existing algorithms, e.g. the generalized likelihood ratio test (GLRT), can be derived from this algorithm by introducing the appropriate limitations.
520 $a The use of subspace models for targets and backgrounds allows detection invariant to changing environmental conditions. The non-Gaussian behavior of target and background distribution residuals complicates the development of subspace-based detection methods. We use discriminant analysis for feature extraction for separating subpixel 3D objects from cluttered backgrounds. The nonparametric estimation of distributions is used to establish the statistical models using the length and direction of residuals. Candidate subspaces are then evaluated to maximize their discriminatory power which is measured between estimated distributions of targets and backgrounds. In this context, a likelihood ratio test is used based on background and mixed statistics for subpixel detection. The detection algorithm is evaluated for HYDICE images and a number of images simulated using DIRSIG under a variety of conditions. The experimental results demonstrate accurate detection performance on these data sets.
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791 $a Ph.D.
792 $a 2007
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