Title :
Unsupervised hyperspectral band selection using parallel processing
Author :
Yang, He ; Du, Qian
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA
Abstract :
Band selection is a common technique to reducing the data dimensionality of hyperspectral imagery. When the desired object information is unknown, the objective of an unsupervised band selection approach is to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following data processing and analysis, the process itself may induce additional computation complexity. In this paper, we propose parallel processing techniques for an unsupervised band selection method without changing band selection result.
Keywords :
data reduction; geophysical signal processing; parallel processing; remote sensing; computational complexity; data dimensionality reduction; hyperspectral imagery; parallel processing; unsupervised hyperspectral band selection; Concurrent computing; Covariance matrix; Data analysis; Data processing; Helium; Hyperspectral imaging; Least squares methods; Linear regression; Parallel processing; Symmetric matrices; band selection; parallel processing;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5417727
