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Some Experiments on Ensembles of Neural Networks for Hyperspectral Image Classification

  • Carlos Hernández-Espinosa
  • Mercedes Fernández-Redondo
  • Joaquín Torres-Sospedra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3213)

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 sens-ing 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.

Keywords

Neural Network Support Vector Machine Spectral Band Hyperspectral Image Ensemble Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Carlos Hernández-Espinosa
    • 1
  • Mercedes Fernández-Redondo
    • 1
  • Joaquín Torres-Sospedra
    • 1
  1. 1.Dept. de Ingeniería y Ciencia de los ComputadoresUniversidad Jaume I.CastellonSpain

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