論文

2019年3月

Snow Cover Estimation from Image Time Series Based on Spectral Unmixing

IEEE Geoscience and Remote Sensing Letters
  • Theo Masson
  • ,
  • Mauro Dalla Mura
  • ,
  • Marie Dumont
  • ,
  • Jocelyn Chanussot

16
3
開始ページ
337
終了ページ
341
記述言語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/LGRS.2018.2873836
出版者・発行元
Institute of Electrical and Electronics Engineers ({IEEE})

© 2004-2012 IEEE. A method based on spectral unmixing (SU) for snow cover estimation from a time series of optical images is proposed in this letter. Specifically, we have developed an endmember estimation procedure that exploits the temporal continuity of a scene. Consecutive dates are jointly processed for a more precise description of background materials improving the estimation of a fractional snow cover map. The proposed workflow relies on up to three consecutive acquisitions over the same area to extract an appropriate set of background spectra to be considered as endmembers. In greater details, multiple sets of endmembers are extracted from different images in the time series by a geometrical automated endmember extraction algorithm and the most relevant one is selected in terms of reconstruction error. Snow cover maps are then estimated by SU considering as endmembers the snow spectra coming from a spectral library and those associated with the background materials as estimated by the proposed procedure. The proposed technique is quantitatively validated considering Moderate-Resolution Imaging Spectroradiometer Terra data over the French Alps and Moroccan High Atlas image time series and comparing the estimated snow cover maps with high-resolution reference data. The experiment clearly demonstrates the effectiveness of the generated set of endmembers using three different approaches to abundance estimation.

リンク情報
DOI
https://doi.org/10.1109/LGRS.2018.2873836
Scopus
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055187551&origin=inward
Scopus Citedby
https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85055187551&origin=inward

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