Volume XLII-4/W14
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 151–157, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-151-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W14, 151–157, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W14-151-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Aug 2019

23 Aug 2019

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

M. Lennert1, T. Grippa1, J. Radoux2, C. Bassine2, B. Beaumont3, P. Defourny2, and E. Wolff1 M. Lennert et al.
  • 1ANAGEO-DGES, Université Libre de Bruxelles, Belgium
  • 2Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
  • 3Remote Sensing and Geodata Unit, Institut Scientifique de Service Public, Liège, Belgium

Keywords: Remote Sensing, Land Cover, Ensemble fusion, Wallonia, GRASS GIS, Orfeo ToolBox

Abstract. 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.