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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Application of Nonlinear Feature Normalization on Combined Hyperspectral and Lidar Data
 Institute of Electrical and Electronics Engineers IEEE; IEEE Geoscience and Remote Sensing Society: IGARSS 2019, IEEE International Geoscience and Remote Sensing Symposium. Proceedings : July 28 August 2, 2019, Yokohama, Japan Piscataway, NJ: IEEE, 2019 ISBN: 9781538691540 ISBN: 9781538691533 ISBN: 9781538691557 pp.11001103 
 International Geoscience and Remote Sensing Symposium (IGARSS) <2019, Yokohama> 

 English 
 Conference Paper 
 Fraunhofer IOSB () 
 feature normalization; mitigating nonlinear effects; Land Cover Classification; training data 
Abstract
Mitigating nonlinear effects, e.g., due to shadows, variations in illumination conditions, and angular dependencies of spectral signatures is an important topic in hyperspectral remote sensing. In this paper, we apply the Nonlinear Feature Normalization on a combined data set consisting of 128 spectral bands and a weighted digital elevation model. The NFN transforms the data set to a new linear basis and by that mitigates nonlinearities. Evaluation is done by applying the Spectral Angle Mapper to the original and the NFNtransformed data. Different parameter combinations are tested to find the best classification results. Additionally, a Random Forest approach is calculated to compare the results.