DocumentCode
762621
Title
A comparative study for orthogonal subspace projection and constrained energy minimization
Author
Du, Qian ; Ren, Hsuan ; Chang, Chein-I
Author_Institution
Dept. of Electr. Eng. & Comput. Sci., Texas A&M Univ., Kingsville, TX, USA
Volume
41
Issue
6
fYear
2003
fDate
6/1/2003 12:00:00 AM
Firstpage
1525
Lastpage
1529
Abstract
We conduct a comparative study and investigate the relationship between two well-known techniques in hyperspectral image detection and classification: orthogonal subspace projection (OSP) and constrained energy minimization. It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR. Based on this relationship, the performance of OSP can be improved via data-whitening and noise-whitening processes.
Keywords
image classification; remote sensing; constrained energy minimization; data-whitening; hyperspectral image classification; hyperspectral image detection; noise-whitening processes; orthogonal subspace projection; Councils; Gaussian noise; Hyperspectral imaging; Maximum likelihood detection; Pixel; Signal to noise ratio; Singular value decomposition; Subspace constraints; Vectors; White noise;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/TGRS.2003.813704
Filename
1220263
Link To Document