A Nonlinear Multiple Feature Learning Classifier for Hyperspectral Images With Limited Training Samples

A nonlinear joint collaborative representation (CR) model with adaptive weighted multiple feature learning to deal with the small sample set problem in hyperspectral image (HSI) classification is proposed. The proposed algorithm first maps every meaningful feature of the image scene into a kernel space by a column-generation (CG)-based technique. A unified multitask learning-based joint CR framework, with adaptive weighting for each feature, is then undertaken by the use of an alternating optimization algorithm, to obtain accurate kernel representation coefficients, which leads to desirable classification results. The experimental results indicate that the proposed algorithm obtains a competitive performance and outperforms the other state-of-the-art regression-based classifiers and the classical support vector machine classifier.