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  4. Feature Concatenation of Hyperspectral and DEM Data for Land Cover Classification
 
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2020
  • Konferenzbeitrag

Titel

Feature Concatenation of Hyperspectral and DEM Data for Land Cover Classification

Abstract
Nonlinear 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.
Author(s)
Gross, Wolfgang
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Bulatov, Dimitri
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Schreiner, Simon
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Middelmann, Wolfgang
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
IGARSS 2020, IEEE International Geoscience and Remote Sensing Symposium. Proceedings
Konferenz
International Geoscience and Remote Sensing Symposium (IGARSS) 2020
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DOI
10.1109/IGARSS39084.2020.9323131
Language
Englisch
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IOSB
Tags
  • Nonlinear Feature Nor...

  • mitigating nonlineari...

  • hyperspectral

  • feature extraction

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