DocumentCode :
2707781
Title :
Locality perserving projections algorithm for hyperspectral image dimensionality reduction
Author :
Wang, Zhiyong ; He, Binbin
Author_Institution :
Inst. of Geo-Spatial Inf. Sci. & Technol., Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2011
fDate :
24-26 June 2011
Firstpage :
1
Lastpage :
4
Abstract :
Feature extraction is an necessary preprocessing step for information extraction from hyperspectral remote sensing data. We exploit locality preserving projections (LPP) for dimensionality reduction, leading to a low-dimensional embedding. Given a subset of hyperspectral image, by LPP, we project them into a low-dimensional space to preserve much of the geometric structure. Hyperspectral image classification is then achieved using Support Vector Machine. To evaluate the proposed method, LPP method was examined in terms of spatial information preservation. The preliminary result of this study demonstrated that it is effective.
Keywords :
feature extraction; geophysical image processing; remote sensing; support vector machines; feature extraction; geometric structure; hyperspectral image classification; hyperspectral image dimensionality reduction; hyperspectral remote sensing data; information extraction; locality preserving projections; locality preserving projections algorithm; spatial information preservation; support vector machine; Feature extraction; Hyperspectral imaging; Laplace equations; Manifolds; Principal component analysis; Locality Preserving Projection; feature extraction; hyperspectral remote sensing; principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoinformatics, 2011 19th International Conference on
Conference_Location :
Shanghai
ISSN :
2161-024X
Print_ISBN :
978-1-61284-849-5
Type :
conf
DOI :
10.1109/GeoInformatics.2011.5980790
Filename :
5980790
Link To Document :
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