Genetic Algorithm Based Feature Subset Selection for Land Cover/ Land Use Mapping Using Wavelet Packet Transform

A genetic algorithm based approach is used in this paper for the selection of a subset from the combination of Wavelet Packet Statistical and Wavelet Packet Co-occurrence textural feature sets to classify the LISS IV satellite images using neural networks. Generally, adding a new feature increases t...

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Vydané v:Journal of the Indian Society of Remote Sensing Ročník 41; číslo 2; s. 237 - 248
Hlavní autori: Rajesh, S, Arivazhagan, S, Moses, K. Pratheep, Abisekaraj, R
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: India Springer-Verlag 01.06.2013
Predmet:
algorithms
discriminant analysis
Earth and Environmental Science
Earth Sciences
land use
neural networks
principal component analysis
remote sensing
Remote Sensing/Photogrammetry
Research Article
wavelet
Normalized difference vegetation index (NDVI)
Wavelet packet co-occurrence feature
Neural networks
Wavelet packet statistical feature
Wavelet packet decomposition
Genetic algorithms
ISSN:0255-660X, 0974-3006
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Shrnutí:A genetic algorithm based approach is used in this paper for the selection of a subset from the combination of Wavelet Packet Statistical and Wavelet Packet Co-occurrence textural feature sets to classify the LISS IV satellite images using neural networks. Generally, adding a new feature increases the complexity of training and classification. Hence there is a need to differentiate between those features that contribute ample information and others. Many current feature reduction techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) involve linear transformations of the original pattern vectors to new vectors of lower dimensions. Hence a multi-objective Genetic Algorithm has been employed to reduce the complexity and increase the accuracy of classification. Four indices - user’s accuracy, producer’s accuracy, overall accuracy and kappa co-efficient are used to assess the accuracy of the classified data. Experimental results show that the proposed Genetic Algorithm approach with lesser number of optimal features produces comparable results with that of our earlier approach using more features.
Bibliografia:http://dx.doi.org/10.1007/s12524-012-0208-5
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ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-012-0208-5