Support Vector Machines In Remote Sensing: The Tricks Of The Trade

Support Vector Machines (SVM) have been widely adopted by the remote sensing community in the last decade. The standard algorithm has been mainly applied to image classification tasks. Many advanced developments based on SVM have been introduced as well. This paper, nevertheless, revises the standard formulation of SVM. An important part of the paper is about the intuition on the SVM parts: the cost, the regularizer and the free parameters. Finally, the paper revises three interesting simple modifications well suited to tackle remote sensing image classification: constraining the margin, including invariances and the information of unlabeled samples. Some examples are given to illustrate these concepts.