Snow cover estimation based on spectral unmixing
| Autor: | Jocelyn Chanussot, J.-P. Dedieu, Pascal Sirguey, Marie Dumont, Miguel Angel Veganzones, T. Masson, M. Dalla Mura |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Field data 0211 other engineering and technologies Mean absolute error Hyperspectral imaging 02 engineering and technology Snow 01 natural sciences Set (abstract data type) Environmental science Fraction (mathematics) Snow cover 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | WHISPERS |
| Popis: | Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers for retrieving snow cover fraction with spectral unmixing. Endmembers can be estimated from on site measurements or estimated directly on the image. In this work we propose a set of endmembers associating semantics of field data for snow endmembers with the extraction of a set in a date without snow for other materials. A heterogeneous area in the Alps was considered in the experiment. Considering reference maps of snow available for several dates, Precision and Mean Absolute Error were computed for evaluating the estimated Snow Cover Fractions. Results obtained confirm the soundness of the proposed approach for low snow fraction. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|