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Proceedings Paper

Subpixel estimation of areal extent of snow cover
Author(s): Alessandra Colombo; Elisabetta Binaghi; Giovanni M. Lechi; Anna Rampini
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Paper Abstract

Land cover-mapping gives base for complicated tasks and high accuracy maps are necessary for reliable global estimations. Land cover mapping involves classification; its accuracy is often affected by mixture presence, which increases with pixel size. Using hard classification approach mixed pixels are source of errors because are treated as pure ones and obtained maps describe scene as made up by mosaic of homogeneous areas. Soft classifications, computing partial belongings of pixels to several categories, are useful tools for dealing with mixture. Two soft classifiers, one based on fuzzy-statistical and one on fuzzy neural network approach, are applied to the classification of NOAA images for snow cover estimation. Results are compared with the traditional hard classifier maximum likelihood. The analysis shows that accuracy of hard classifiers is greatly affected by increasing of pixel size. Soft classifiers perform better accuracy then hard classifier in areal estimation. Particularly fuzzy-statistical classifier gives better results then fuzzy neural network but it requires mixture information that fuzzy neural network does not need. Fuzzy neural network results the best tool to classify low resolution images for the evaluation of snow covered area as it performs the best balancing between minimal requested ground truth and high accuracy.

Paper Details

Date Published: 17 December 1999
PDF: 11 pages
Proc. SPIE 3868, Remote Sensing for Earth Science, Ocean, and Sea Ice Applications, (17 December 1999); doi: 10.1117/12.373098
Show Author Affiliations
Alessandra Colombo, Politecnico di Milano (Italy)
Elisabetta Binaghi, Istituto per le Tecnologie Informatiche Multimediali (Italy)
Giovanni M. Lechi, Politecnico di Milano (Italy)
Anna Rampini, Istituto per le Tecnologie Informatiche Multimediali (Italy)


Published in SPIE Proceedings Vol. 3868:
Remote Sensing for Earth Science, Ocean, and Sea Ice Applications
Giovanna Cecchi; Edwin T. Engman; Eugenio Zilioli, Editor(s)

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