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Automatic target detection in hypers...

Alarcon Ramirez, Andres.

Automatic target detection in hyperspectral images using level sets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automatic target detection in hyperspectral images using level sets.
作者:	Alarcon Ramirez, Andres.
面頁冊數:	118 p.
附註:	Source: Masters Abstracts International, Volume: 48-02, page: .
附註:	Adviser: Vidya Manian.
Contained By:	Masters Abstracts International48-02.
標題:	Engineering, Computer.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1471646
ISBN:	9781109444209

Automatic target detection in hyperspectral images using level sets.
Alarcon Ramirez, Andres.

Automatic target detection in hyperspectral images using level sets. - 118 p.

Source: Masters Abstracts International, Volume: 48-02, page: .

Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2010.

A novel variational method using level sets that incorporate spectral angle distance in the model for automatic target detection is presented. Algorithms are presented for detecting both spatial and pixel targets. The new method is tested in tasks of unsupervised target detection in hyperspectral images with more than 100 bands, and the results are compared with a widely used region-based level sets algorithm. In addition Texture and spectral information are incorporated into level set equation for extracting large targets placed on images The proposed method is also adapted for supervised target detection and its performance is compared with traditional orthogonal subspace projection and constrained signal detector for the detection of pixel targets. The method is evaluated with different complexity such as noise levels and target sizes.

ISBN: 9781109444209Subjects--Topical Terms:

384375
Engineering, Computer.

Automatic target detection in hyperspectral images using level sets.
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520 $a A novel variational method using level sets that incorporate spectral angle distance in the model for automatic target detection is presented. Algorithms are presented for detecting both spatial and pixel targets. The new method is tested in tasks of unsupervised target detection in hyperspectral images with more than 100 bands, and the results are compared with a widely used region-based level sets algorithm. In addition Texture and spectral information are incorporated into level set equation for extracting large targets placed on images The proposed method is also adapted for supervised target detection and its performance is compared with traditional orthogonal subspace projection and constrained signal detector for the detection of pixel targets. The method is evaluated with different complexity such as noise levels and target sizes.
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