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  4. Application of Nonlinear Feature Normalization on Combined Hyperspectral and Lidar Data
 
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2019
Konferenzbeitrag
Titel

Application of Nonlinear Feature Normalization on Combined Hyperspectral and Lidar 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 NFN-transformed data. Different parameter combinations are tested to find the best classification results. Additionally, a Random Forest approach is calculated to compare the results.
Author(s)
Gross, Wolfgang
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Bulatov, Dimitri
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Solbrig, Peter
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
IGARSS 2019, IEEE International Geoscience and Remote Sensing Symposium. Proceedings
Konferenz
International Geoscience and Remote Sensing Symposium (IGARSS) 2019
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DOI
10.1109/IGARSS.2019.8898979
Language
Englisch
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IOSB
Tags
  • feature normalization...

  • mitigating nonlinear ...

  • Land Cover Classifica...

  • training data

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