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An open-source semi-automated processing chain for urban obia classification

par Grippa, Taïs ;Lennert, Moritz ;Beaumont, Benjamin ;Vanhuysse, Sabine ;Stephenne, Nathalie ;Wolff, Eléonore
Référence GEOBIA 2016 : Solutions and Synergies(14-16 September 2016: University of Twente Faculty of Geo-Information and Earth Observation (ITC)), GEOBIA 2016 : Solutions and Synergies
Publication Publié, 2016-09-16

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Résumé :

This study presents the development of a semi-automated processing chain for OBIA urban land-cover and land-use classification. Implemented in Python and relying on existing open-source software GRASS GIS and R. The complete tool chain is available in open-access and adaptable to specific user needs. For automation purpose, we developed two GRASS GIS add-ons allowing (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual machine learning classifiers or their predictions combination through voting-schemes. We tested the performance and transferability of the processing chain using sub-metric multispectral and height data on two very different urban environments: Ouagadougou, Burkina Faso in sub-Saharan Africa and Liège, Belgium in Western Europe. Using a hierarchical classification scheme, the kappa values reached for both cities about 0.78 at the second level (9 and 11 classes) and 0.90 at the first level (5 classes).