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Proceedings Paper

A variational method for target detection in hyperspectral images
Author(s): Andrés Alarcón; Vidya Manian
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Paper Abstract

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. Additionally, techniques of band subset selection are evaluated for the reduction of data dimensionality. The proposed method is 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.

Paper Details

Date Published: 14 April 2008
PDF: 13 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670C (14 April 2008); doi: 10.1117/12.776698
Show Author Affiliations
Andrés Alarcón, Univ. of Puerto Rico at Mayagüez (United States)
Vidya Manian, Univ. of Puerto Rico at Mayagüez (United States)

Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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