Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 161-166, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-161-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
30 Jul 2012
CLASSIFICATION OF AIRBORNE LASER SCANNING DATA IN WADDEN SEA AREAS USING CONDITIONAL RANDOM FIELDS
A. Schmidt, F. Rottensteiner, and U. Sörgel Institute of Photogrammetry and GeoInformation, University of Hannover, Germany
Keywords: LiDAR, classification, conditional random fields, coast Abstract. In this paper we investigate the influence of contextual knowledge for the classification of airborne laser scanning data in Wadden Sea areas. For this propose we use Conditional Random Fields (CRF) for the classification of the point cloud into the classes water, mudflat, and mussel bed based on geometric and intensity features. We learn typical structures in a training step and combine local descriptors with context information in a CRF framework. It is shown that the point-based classification result, especially the completeness rate for water and mussel bed as well as the correction rate of water, can be significantly improved if contextual knowledge is integrated. We evaluate our approach on a test side of the German part of the Wadden Sea and compare the results with a Maximum Likelihood Classification.
Conference paper (PDF, 932 KB)


Citation: Schmidt, A., Rottensteiner, F., and Sörgel, U.: CLASSIFICATION OF AIRBORNE LASER SCANNING DATA IN WADDEN SEA AREAS USING CONDITIONAL RANDOM FIELDS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 161-166, https://doi.org/10.5194/isprsarchives-XXXIX-B3-161-2012, 2012.

BibTeX EndNote Reference Manager XML