Stefan R. Sandmeier

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— A new field goniometer system (FIGOS) is introduced that allows in situ measurements of hyperspectral bidirec-tional reflectance data under natural illumination conditions. Hy-perspectral bidirectional reflectance distribution function (BRDF) data sets taken with FIGOS nominally cover the spectral range between 300 and 2450 nm in 704 bands. Typical(More)
A physically-based model to correct atmospheric and topographically induced illumination effects in optical satellite data is developed and tested. Special emphasis is put on the impact of rugged terrain. Ground reference data for various land use classes enables the assessment of the corrections' influence on land use classifications. The estimation of(More)
The reflectance characteristics of most natural objects vary with illumination and viewing geometry, i.e. expose a non-Lambertian behaviour. New sensor systems are capable to view at targets quasi-simultaneously from nadir and different off-nadir positions. In regard to radiometric corrections the Lambertian assumption has to be overcome by detailed(More)
This paper presents results from analyzing hyperspectral BRDF data of grass lawn and watercress. The intensity of the hotspot as a function of wavelength is determined from fitting an empirical (or rather phenomenological) model to the data. The model consists of a lambertian, bowlshape, hotspot and forward scattering component. An analytical, theoretically(More)
Four radiometric correction methods for the reduction of slope-aspect effects in a Landsat TM data set are tested in a mountainous test site with regard to their physical soundness, their influence on a forest classification as well as on the visual appearance of the scene. Excellent ground reference information and a fine resolution DEM allowed a precise(More)
The integration of multi-source ancillary data is one of the most promising techniques for improved classification of remote sensing images. In this research a classification strategy based on possibility theory and fuzzy subsets was applied in order to combine spectral and non-spectral information. The non-spectral information was used to define expert(More)
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