Simplifying Support Vector Machines for classification of hyperspectral imagery and selection of relevant features

Support Vector Machines (SVM) for image classification proved to perform well in many applications. However, they are often not preferred in hyperspectral image analysis due to long processing times caused by a high number of support vectors and large data sets. We present two approaches that speed-up the classification process with SVM by a) simplifying the original SVM, i.e. reducing the number of support vectors, and b) reducing the number of features by selecting relevant, non-redundant features. Results for three classification problems are shown. By applying the two approaches, we observe reduction rates a) between 9.1% and 27.2% for the number of support vectors and b) from 86.8% to 93.0% of features, both without significant decreases in classification accuracy. This enables a fast mapping of complete hyperspectral scenes.