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Automatic modeling of nonlinear signal source variations in hyperspectral data

: Gross, Wolfgang; Keskin, Goksu; Schilling, Hendrik; Middelmann, Wolfgang


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Geoscience and Remote Sensing Society:
IGARSS 2014, International Geoscience and Remote Sensing Symposium. Proceedings : 13-18 July 2014, Quebec City, QC, 35th Canadian Symposium on Remote Sensing
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-5775-0
International Geoscience and Remote Sensing Symposium (IGARSS) <2014, Quebec>
Canadian Symposium on Remote Sensing <35, 2014, Quebec>
Conference Paper
Fraunhofer IOSB ()
hyperspectral; manifold learning; nonlinear modeling; skeletonizing

Nonlinear effects in hyperspectral data complicate classification and other data analysis procedures. Transforming the data onto manifolds can help to improve the results while simultaneously reducing the dimensionality due to the high correlation among the spectral bands. Methods like ISOMAP or Locally Linear Embedding are not ideal when the data is degraded by noise. In this paper, a method is introduced to automatically generate support points for skeletonizing a highdimensional point cloud. The skeleton is identified with multiple signal source variations of distinct materials and can be used to transform the data to improve further analysis procedures.