Gross, WolfgangWolfgangGrossBulatov, DimitriDimitriBulatovSchreiner, SimonSimonSchreinerMiddelmann, WolfgangWolfgangMiddelmann2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/41010710.1109/IGARSS39084.2020.9323131Nonlinear effects in hyperspectral (HS) remote sensing data, caused by shadows, varying illumination conditions, as well as by directional reflectance variations, may lead to in accurate land cover classification. Including additional features of a simultaneously collected digital elevation model (DEM) generally improves the results. In this paper, we apply the Nonlinear Feature Normalization (NFN) to a weighted concatenation of HS channels and different sets of features derived from DEMs to improve the classification accuracy. The evaluation is performed on two data sets, where, the labeled data for one of them was derived using an interactive approach based on unsupervised classification. Using sensor data fusion and NFN transformation improved classification accuracy from a Cohens k of 0.6 to values over 0.8.enNonlinear Feature Normalizationmitigating nonlinearitieshyperspectralfeature extraction004670conference paper