Abstract
In this paper the classification of hyperspectral images is investigated by using a supervised approach. The spectral feature are extracted with well known decision boundary feature extraction (DBFE) and non-parametric weighted feature extraction (NWFE) techniques. The most informative features are fed to random forest (RF) classifier to perform pixel-wise classification. The experiments are carried out on two benchmark hyperspectral images. The results show that RF classifier generates good classification accuracies for hyperspectral image with smaller execution time. Among feature extraction techniques, DBFE has produced better results than NWFE.
Published Version
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