Classification of Hyperspectral Images with Nonlinear Filtering and Support Vector Machines

Abstract

Support Vector Machines, recently introduced in hyperspectral imagery, are applied to classify land cover on images from the airborne CASI sensor with a small training set. A smoothing preprocessing step is achieved, based on a vectorial extension of the anisotropic diffusion nonlinear filtering process. It allows the separability of the classes to be increased as well as homogeneous areas to be smoothed. It comes to take into consideration the spatial context before the classification, leading to improve the classification rate and to produce noiselessy classification maps with support vector machines