Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 17-24, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/17/2014/
doi:10.5194/isprsarchives-XL-3-17-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
A two-layer Conditional Random Field model for simultaneous classification of land cover and land use
L. Albert, F. Rottensteiner, and C. Heipke Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hanover, Germany
Keywords: Contextual classification, Conditional Random Fields, multi-layer, land use classification Abstract. This paper proposes a two-layer Conditional Random Field model for simultaneous classification of land cover and land use. Both classification tasks are integrated into a unified graphical model, which is reasonable due to the fact that land cover and land use exhibit strong contextual dependencies. In the CRF, we distinguish a land cover layer and a land use layer. Both layers differ with respect to the entities corresponding to the nodes and the classes to be distinguished. In the land cover layer, the nodes correspond to superpixels extracted from the image data, whereas in the land use layer the nodes correspond to objects of a geospatial land use database. Statistical dependencies between land cover and land use are explicitly modelled as pair-wise potentials. Thus, we obtain a consistent model, where the relations between land cover and land use are learned from representative training data. The approach is designed for input data based on aerial images. Experiments are performed on an urban test site. The experiments show the feasibility of the combination of both classification tasks into one overall approach and investigate the influence of the size of the superpixels on the classification result.
Conference paper (PDF, 472 KB)


Citation: Albert, L., Rottensteiner, F., and Heipke, C.: A two-layer Conditional Random Field model for simultaneous classification of land cover and land use, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 17-24, doi:10.5194/isprsarchives-XL-3-17-2014, 2014.

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