DocumentCode :
2425425
Title :
Band selection based on evolution algorithm and sequential search for hyperspectral classification
Author :
Huang, Rui ; Li, Xianhua
Author_Institution :
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai
fYear :
2008
fDate :
7-9 July 2008
Firstpage :
1270
Lastpage :
1273
Abstract :
Band (feature) selection for multispectral or hyperspectral data is an effective method to reduce dimension for cutting down the computational cost and alleviating the Hughes phenomenon. An efficient feature selection method based on evolution algorithm (PSO and GA) and sequential search is proposed. The method embeds the sequential search into the evolution optimization for better ability of the fine tune in local search space and thus behaves well in both global and local cases. In addition, the embed scheme guarantees the validity of solutions for the 1-st form feature selection problem. The experiments with an airborne visible/infrared imaging spectrometer (AVIRIS) data set show the effectiveness of the proposed method.
Keywords :
evolutionary computation; image classification; infrared imaging; search problems; Hughes phenomenon; airborne visible data set; band feature selection; evolution algorithm; evolution optimization; hyperspectral classification; infrared imaging spectrometer data set; multispectral data; sequential search; Computational efficiency; Data engineering; Hyperspectral imaging; Infrared imaging; Infrared spectra; Neural networks; Optimization methods; Particle swarm optimization; Remote monitoring; Spectroscopy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Audio, Language and Image Processing, 2008. ICALIP 2008. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1723-0
Electronic_ISBN :
978-1-4244-1724-7
Type :
conf
DOI :
10.1109/ICALIP.2008.4590145
Filename :
4590145
Link To Document :
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