  Nowadays, the hyperspectral remote sensing imagery HSI becomes an important
tool to observe the Earth's surface, detect the climatic changes and many other
applications. The classification of HSI is one of the most challenging tasks
due to the large amount of spectral information and the presence of redundant
and irrelevant bands. Although great progresses have been made on
classification techniques, few studies have been done to provide practical
guidelines to determine the appropriate classifier for HSI. In this paper, we
investigate the performance of four supervised learning algorithms, namely,
Support Vector Machines SVM, Random Forest RF, K-Nearest Neighbors KNN and
Linear Discriminant Analysis LDA with different kernels in terms of
classification accuracies. The experiments have been performed on three real
hyperspectral datasets taken from the NASA's Airborne Visible/Infrared Imaging
Spectrometer Sensor AVIRIS and the Reflective Optics System Imaging
Spectrometer ROSIS sensors. The mutual information had been used to reduce the
dimensionality of the used datasets for better classification efficiency. The
extensive experiments demonstrate that the SVM classifier with RBF kernel and
RF produced statistically better results and seems to be respectively the more
suitable as supervised classifiers for the hyperspectral remote sensing images.
  Keywords: hyperspectral images, mutual information, dimension reduction,
Support Vector Machines, K-Nearest Neighbors, Random Forest, Linear
Discriminant Analysis.
