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- Title
ARCHETYPAL ANALYSIS FOR SPARSE REPRESENTATION-BASED HYPERSPECTRAL SUB-PIXEL QUANTIFICATION.
- Authors
Drees, Lukas; Roscher, Ribana
- Abstract
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.
- Subjects
HYPERSPECTRAL imaging systems; PIXELS; LAND cover
- Publication
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2017, Vol 4, Issue 1/W1, p133
- ISSN
2194-9042
- Publication type
Article
- DOI
10.5194/isprs-annals-IV-1-W1-133-2017