AbstractHyperspectral sensors are becoming cheaper, faster and more readily available. Apart from industry applications, manufacturers push to bring compact devices into the end-consumer market. This development gives rise to many interesting applications such as the identification of counterfeit pharmaceutical products or the classification of food stuffs. These applications require precise models of the underlying classes. However, building these models from expert knowledge is not feasible. In this paper, we propose to use machine learning techniques to infer a model of many classes from an annotated dataset instead. We investigate the use of three popular methods: support vector machines, random forest classifiers and partial least squares. In contrast to similar approaches using support vector machines, we restrict ourselves to the linear formulation and train the classifiers by solving the primal, instead of dual optimization problem. Our experiments on a large dataset show that the support vector machine approach is superior to random forests and partial least squares in classification accuracy as well as training time.
Classification of Remotely Sensed Algal Blooms along the Coast of India using Support Vector Machines and Regularized Least Squares
