A SVMS-based hyperspectral data classification algorithm in a similarity space

In this paper a multi-steps algorithm based on Support Vectors Machines (SVMs) in similarity space is proposed. The SVMs is used as a recent classification method and separation boundary estimation technique for high dimensional data. It benefits of limited number of data for training of supervised classification, which is a key challenge in hyperspectral data analysis.