5 research outputs found
ARCHETYPAL ANALYSIS FOR SPARSE REPRESENTATION-BASED HYPERSPECTRAL SUB-PIXEL QUANTIFICATION
This paper focuses on the quantification of land cover fractions in an urban area of Berlin, Germany, using simulated hyperspectral
EnMAP data with a spatial resolution of 30m×30m. For this, sparse representation is applied, where each pixel with unknown surface
characteristics is expressed by a weighted linear combination of elementary spectra with known land cover class. The elementary
spectra are determined from image reference data using simplex volume maximization, which is a fast heuristic technique for archetypal
analysis. In the experiments, the estimation of class fractions based on the archetypal spectral library is compared to the estimation
obtained by a manually designed spectral library by means of reconstruction error, mean absolute error of the fraction estimates, sum
of fractions and the number of used elementary spectra. We will show, that a collection of archetypes can be an adequate and efficient
alternative to the spectral library with respect to mentioned criteria