DocumentCode
3367352
Title
On the performance of random-projection-based dimensionality reduction for endmember extraction
Author
Du, Qian ; Fowler, James E.
Author_Institution
Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA
fYear
2010
fDate
25-30 July 2010
Firstpage
1277
Lastpage
1280
Abstract
In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction is capable of providing better endmembers after effective decision fusion.
Keywords
data reduction; feature extraction; image fusion; random processes; decision fusion; dimensionality reduction; hyperspectral endmember extraction; random projection; Algorithm design and analysis; Data mining; Hyperspectral imaging; Lakes; Pixel; Principal component analysis; dimensionality reduction; endmember extraction; hyperspectral imagery; random projection;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International
Conference_Location
Honolulu, HI
ISSN
2153-6996
Print_ISBN
978-1-4244-9565-8
Electronic_ISBN
2153-6996
Type
conf
DOI
10.1109/IGARSS.2010.5653584
Filename
5653584
Link To Document