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
fDate :
6/1/2003 12:00:00 AM
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;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
DOI :
10.1109/TGRS.2003.813704
