NDLI: Genetic algorithm based new sequence of principal component regression (GA-NSPCR) for feature selection and yield prediction using hyperspectral remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Mulyono, S. Fanany, M.I. Basaruddin, T.
Copyright Year	2012
Description	Author affiliation: Faculty of Computer Science, Universitas Indonesia, Indonesia (Mulyono, S.; Fanany, M.I.; Basaruddin, T.)
Abstract	Recently, hyperspectral images are used to estimate the yield of food crops. The images consist of a large number of bands which requires sophisticated method for its analysis. One approach to reduce computational cost and to accelerate knowledge discovery is by eliminating bands that do not add value to the analysis. In this paper, a genetic algorithm based new sequence of principal component regression (GA-NSPCR) method is proposed and tested using 116 band HyMap airborne hyperspectral data and yield data collected from paddy fields. The proposed method uses GA to select an initial subset of hyperspectral bands, and subsequently generate a more accurate subset by measuring the minimum error of prediction model defined by principal component regression (PCR). Unlike standard PCR methods which order the features based on singular values, in each generation NSPCR orders the features based on squared multiple correlation coefficient $R^{2}.$ Yield data and spectral data are used to generate a separate training and testing dataset using 8 times bootstrap resampling (8-rounds BSR) to deal with limited number of samples in training data. Differed from standard GA impelementation, the fitness function evaluates three Lp-norms to obtain the best prediction model.
Starting Page	4198
Ending Page	4201
File Size	298327
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351743
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Hyperspectral imaging Predictive models Principal component analysis Mathematical model Jacobian matrices yield data hyperspectral genetic algorithm principal component regression
Content Type	Text
Resource Type	Article

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