DocumentCode :
969447
Title :
Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery
Author :
Zhang, Liangpei ; Zhong, Yanfei ; Huang, Bo ; Gong, Jianya ; Li, Pingxiang
Author_Institution :
Wuhan Univ., Wuhan
Volume :
45
Issue :
12
fYear :
2007
Firstpage :
4172
Lastpage :
4186
Abstract :
A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.
Keywords :
geophysical signal processing; geophysical techniques; optimisation; remote sensing; antigenic stimulus; artificial immune system; clonal selection feature selection algorithm; clonal-selection feature-weighting algorithm; decision boundary feature extraction; dimensionality reduction; feature weighting algorithm; genetic algorithm based feature selection; hyperspectral imagery; immune response; optimization problem; remote sensing imagery; sequential forward floating selection; sequential forward selection; Artificial immune systems; Feature extraction; Hyperspectral imaging; Hyperspectral sensors; Independent component analysis; Infrared imaging; Infrared spectra; Remote monitoring; Remote sensing; Stochastic systems; Artificial immune system (AIS); artificial intelligence; clonal selection; dimensionality reduction; feature selection; remote sensing;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2007.905311
Filename :
4378565
Link To Document :
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