An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification

Taïs Grippa, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, Eléonore Wolff
<span title="2017-04-11">2017</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. The processing chain is implemented in Python and relies on existing open-source software GRASS GIS and R. The complete tool chain is available in open access and is adaptable to specific user needs. For automation purposes, we developed two GRASS GIS add-ons enabling users (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify
more &raquo; ... ote sensing data using several individual machine learning classifiers or their prediction combinations through voting-schemes. We tested the performance 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 overall accuracy reached 93% at the first level (5 classes) and about 80% at the second level (11 and 9 classes, respectively).
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs9040358">doi:10.3390/rs9040358</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q65vsz34trfyxd6ntuu6lxgxca">fatcat:q65vsz34trfyxd6ntuu6lxgxca</a> </span>
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