Hyperspectral image classification by ensembles of multilayer feedforward networks. Fernandez-Redondo, M., Hernandez-Espinosa, C., & Torres-Sospedra, J. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), volume 2, pages 1145-1149, 2004. IEEE.
Hyperspectral image classification by ensembles of multilayer feedforward networks [link]Website  abstract   bibtex   
A hyperspectral image is used in remote sensing to identify different type of coverts on the Earth surface. It is composed of pixels and each pixel consist of spectral bands of the electromagnetic reflected spectrum. Neural networks and ensemble techniques have been applied to remote sensing images with a low number of spectral bands per pixel (less than 20). In this paper we apply different ensemble methods of Multilayer Feedforward networks to images of 224 spectral bands per pixel, where the classification problem is clearly different. We conclude that in general there is an improvement by the use of an ensemble. For databases with low number of classes and pixels the improvement is lower and similar for all ensemble methods. However, for databases with a high number of classes and pixels the improvement depends strongly on the ensemble method. We also present results of classification of support vector machines (SVM) and see that a neural network is a useful alternative to SVM.
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 title = {Hyperspectral image classification by ensembles of multilayer feedforward networks},
 type = {inProceedings},
 year = {2004},
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 pages = {1145-1149},
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 publisher = {IEEE},
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 abstract = {A hyperspectral image is used in remote sensing to identify different type of coverts on the Earth surface. It is composed of pixels and each pixel consist of spectral bands of the electromagnetic reflected spectrum. Neural networks and ensemble techniques have been applied to remote sensing images with a low number of spectral bands per pixel (less than 20). In this paper we apply different ensemble methods of Multilayer Feedforward networks to images of 224 spectral bands per pixel, where the classification problem is clearly different. We conclude that in general there is an improvement by the use of an ensemble. For databases with low number of classes and pixels the improvement is lower and similar for all ensemble methods. However, for databases with a high number of classes and pixels the improvement depends strongly on the ensemble method. We also present results of classification of support vector machines (SVM) and see that a neural network is a useful alternative to SVM.},
 bibtype = {inProceedings},
 author = {Fernandez-Redondo, M. and Hernandez-Espinosa, C. and Torres-Sospedra, J.},
 booktitle = {2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)}
}

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