Some Experiments on Ensembles of Neural Networks for Hyperspectral Image Classification

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.