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Derivatives of spectral reflectance signatures can capture salient features of different land-cover classes. Such information has been used for supervised classification of remote sensing data along with spectral reflectance. In the paper, we study how supervised classification of hyperspectral remote sensing data can benefit from the use of derivatives of spectral reflectance without the aid of other techniques, such as dimensionality reduction and data fusion. An empirical conclusion is given based on a large amount of experimental evaluations carried out on three real hyperspectral remote sensing data sets. The experimental results show that when a training data set is of a small size or the quality of the data is poor, the use of additional first order derivatives can significantly improve classification accuracies along with original spectral features when using classifiers which can avoid the “curse of dimensionality,” such as the SVM algorithm.