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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Geoscience and Remote Sensing, 48, 2297-2307 (2010 The manuscript of the above article revised after peer review and submitted to the journal for publication, follows. Please note that small changes may have been made after submission and the definitive version is that subsequently published as: claims that the method is insensitive to the dimensionality of the data and so not requiring a dimensionality reduction analysis in pre-processing. Here, a series of classification analyses with two hyperspectral sensor data sets reveal that the accuracy of a classification by a SVM does vary as a function of the number of features used. IEEE Transactions onCritically, it is shown that the accuracy of a classification may decline significantly (at 0.05 level of statistical significance) with the addition of features, especially if a small training sample is used. This highlights a dependency of the accuracy of classification by a SVM on the dimensionality of the data and so the potential value of undertaking a feature selection analysis prior to classification. Additionally, it is demonstrated that even when a large training sample is available feature selection may still be useful. For example, the accuracy derived from the use of a small number of features may be noninferior (at 0.05% level of significance) to that derived from the use of a larger feature set providing potential advantages in relation to issues such as data storage and computational processing costs. Feature selection may, therefore, be a valuable analysis to include in pre-processing operations for classification by a SVM.

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