Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 421942
Title Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty
Author(s) Beekhuizen, J.; Clarke, K.C.
Source International Journal of applied Earth Observation and Geoinformation 12 (2010)3. - ISSN 0303-2434 - p. 127 - 137.
DOI http://dx.doi.org/10.1016/j.jag.2010.01.005
Department(s) Wageningen University
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) remotely sensed data - image classification - textural features - fuzzy-sets - cover - information - optimization - areas - maps
Abstract The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes
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