Hyperspectral Image Classification Using Support Vector Machines with an Efficient Principal Component Analysis Scheme

Support vector machines (SVM) together with principal component analysis (PCA) have been applied to hyperspectral image classification and mapping with great success. PCA has been proved to be an effective preprocessing tool for dimension reduction and/or feature extraction. After dimension reduction with PCA, the classification and mapping time can be dramatically reduced while retaining good accuracy. However, the computational cost of PCA preprocessing can be as high as that of SVM classification applied to the original unreduced data set. Researchers have studied different algorithms to cut the PCA preprocessing time, while some others totally ignored that cost. We propose a simple PCA preprocessing scheme which can reduce the computational complexity many folds and is particularly suitable for image data of large size. High classification accuracy can be achieved. A numerical example on an Earth Observing-1 (EO-1) Hyperion image is included to demonstrate the viability of this new procedure. In addition, this example clearly shows that the standard PCA preprocessing may require as much time as the SVM classification does.