Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data

Support vector machine (SVM) is widely used in classification of hyperspectral reflectance data. In traditional SVM, features are generated from all or subsets of spectral bands with each feature contributing equally to the classification. In classification of small hyperspectral reflectance data sets, a common challenge is Hughes phenomenon, which is caused by many redundant features and resulting in subsequent poor classification accuracy. In this study, we examined two approaches to assigning weights to SVM features to increase classification accuracy and reduce adverse effects of Hughes phenomenon: 1) "RSVM" refers to support vector machine with relief feature weighting algorithm, and 2) "FRSVM" refers to support vector machine with fuzzy relief feature weighting algorithm. We used standardized weights to extract a subset of features with high classification contribution. Analyses were conducted on a reflectance data set of individual corn kernels from three inbred lines and a public data set with three selected land-cover classes. Both weighting methods and reduction of features increased classification accuracy of traditional SVM and therefore reduced adverse effects of Hughes phenomenon.