Feature selection for hyperspectral data based on recursive support vector machines

In this article, a feature selection algorithm for hyperspectral data based on a recursive support vector machine (R-SVM) is proposed. The new algorithm follows the scheme of a state-of-the-art feature selection algorithm, SVM recursive feature elimination or SVM-RFE, and uses a new ranking criterion derived from the R-SVM. Multiple SVMs are used to address the multiclass problem. The algorithm is applied to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to select the most informative bands and the resulting subsets of the bands are compared with SVM-RFE using the accuracy of classification as the evaluation of the effectiveness of the feature selection. The experimental results for an agricultural case study indicate that the feature subset generated by the newly proposed algorithm is generally competitive with SVM-RFE in terms of classification accuracy and is more robust in the presence of noise.