An Assessment of the Impact of Dimensionality Reduction on the Speed and Accuracy of Hyperspectral Image Classification
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چکیده
This paper investigates the extent to which the accuracy and speed of classifying Hyperspectral remote sensing images are affected by the application of varying degrees of dimensionality reduction. Three methods have been used for dimensionality reduction: PCA, ICA and random band selection; SVM has been used for classification. The results have been evaluated on both natural and man-made scenarios. Keywords— hyperspectral, dimensionality reduction, classification, support vector machine, sequential minimal optimization, PCA, ICA
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تاریخ انتشار 2013