Classification of airborne laser scanning data in wadden sea areas using conditional random fields

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.