Simplifying Support Vector Machines for Regression analysis of hyperspectral imagery

Support Vector Machines for Regression (SVR) proved to perform well. However, they are not preferred in image analysis due to a high number of needed support vectors (SV) and consequently long processing times. We present a method for simplifying the original SVR regression function up to a user-specified degree of accepted performance decrease. We show results for two regression problems: modelling leaf area index and dry vegetation mixing fraction using simulated hyperspectral EnMAP data. In both cases, SVR demonstrate high potential for modelling complex dependencies between hyperspectral reflectance and quantitative targets. By simplifying the original SVR, we observed reduction rates in number of SV in the 86% to 95% range for acceptable degrees of approximation quality. This enables a fast mapping of complete EnMAP scenes.