CREATING WALLONIA'S NEW VERY HIGH RESOLUTION LAND COVER MAPS: COMBINING GRASS GIS OBIA AND OTB PIXEL-BASED RESULTS

CREATING WALLONIA'S NEW VERY HIGH RESOLUTION LAND COVER MAPS: COMBINING GRASS GIS OBIA AND OTB PIXEL-BASED RESULTS

The Walloon region of Belgium has launched a research project that aims at elaborating a methodology for automated, high-quality land cover mapping, based primarily on its yearly 0.25m orthophoto coverage. Whereas in urban areas an object-based (OBIA) approach has been the privileged path in the las...

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Bibliographic Details
Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-4/W14; pp. 151 - 157
Main Authors: Lennert, M., Grippa, T., Radoux, J., Bassine, C., Beaumont, B., Defourny, P., Wolff, E.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 23-08-2019
Copernicus Publications
Subjects:
Accuracy
Archives & records
Automation
Datasets
Decision making
Deep learning
Dempster-Shafer Method
Freeware
Geographical information systems
Land cover
Machine learning
Mapping
Open data
Open source software
Photogrammetry
Pixels
Remote sensing
Research projects
Resolution
Rural areas
Satellites
Segmentation
Temporal resolution
Urban areas
User needs
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Summary:The Walloon region of Belgium has launched a research project that aims at elaborating a methodology for automated, high-quality land cover mapping, based primarily on its yearly 0.25m orthophoto coverage. Whereas in urban areas an object-based (OBIA) approach has been the privileged path in the last years as it allows taking into account shape information relevant for the characterization of man-made constructions, such an approach has its limits in the rural and more natural areas due to increased difficulties for segmentation and less sharp boundaries, thus calling for a pixel-based approach. The project thus consists in developing a combination of methods, and to integrate their results through an ensemble fusion approach. As many of the more natural land cover classes have temporal profiles which cannot be detected in a one-date orthoimage, Sentinel 1 and 2 data are also included in order to take advantage of their higher spectral and temporal resolution. All methods are trained using existing regional databases. In a second step, we combine the different LC classification results by fusioning them into one high-accuracy (over 90% OA) product, using a series of different approaches ranging from rule-based to machine learning to the Dempster-Shafer method. The entire toolchain is based on free and open source software, mainly GRASS GIS and Orfeo ToolBox. Results indicate the importance of the quality of the individual classifications for the fusion results and justify the choice of combining OBIA and pixel-based approaches in order to avoid the pitfalls of each.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-4-W14-151-2019