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Library Improved Band Selection Technique for Hyperspectral Data Using Factor Analysis

Improved Band Selection Technique for Hyperspectral Data Using Factor Analysis

Improved Band Selection Technique for Hyperspectral Data Using Factor Analysis

Resource information

Date of publication
december 2013
Resource Language
ISBN / Resource ID
AGRIS:US201600069012
Pages
199-211

This paper discusses a statistical and band transformation based approach to select bands for hyperspectral image analysis. Hyperspectral images contain large number of spectral bands with redundant information about the spectral classes in the image scene. It is necessary to reduce the high dimensionality of the data for the processing of hyperspectral data. We report a feature selection technique that removes correlated spectral bands using band decorrelation technique and obtains maximum variance image bands based on factor analysis. Factor analysis method of band selection technique is also validated against existing methods of band selection. The study is carried out for the agriculturally rich area of Musiri region of South India that has varied landcover types. Evaluation of the band selection procedure is done using signature separability measures such as Euclidean distance, Divergence, Transformed divergence and Jeffries Matusita distance. Results indicated that selected bands exhibited maximum separability and also occurred predominantly at wavelength 700 nm, 850, 1000 nm, 1200 nm, 1648 nm and 2200 nm.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Lavanya, A.
Sanjeevi, S.

Publisher(s)
Data Provider