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4.2 Classification results 
For the investigation of the potential of context integration for 
the classification of LIDAR data in Wadden Sea areas, we 
compare three classification results. Firstly, we apply a state-of- 
the-art classifier, Maximum Likelihood Classification, in order 
to evaluate our method. Secondly, we show the results of our 
CRF based approach. Since we are interested in the 
investigation of influence of contextual knowledge for the 
classification, thirdly, we increase the value of the 
neighbourhood N from N = 2 to N = 4. Table 1 and Figure 5 
depict the classification results. 
For mudflat areas, we achieve more than 90% completeness and 
more than 94% correctness in all three tests. Thereby, the 
incorporation of context in our approach helps increasing the 
correctness compared to the Maximum Likelihood 
Classification about 3 - 4%. 
In comparison to these results, the rates for correctness of water 
areas detection are not on the same level (between 52% and 
71%). In particular the discrimination of water and mudflat 
leads to a certain rate of misclassification. For Maximum 
Likelihood, the classification of water areas often fails in the 
transition zone between water and mudflat where elevation 
differences are low. Moreover, some water areas in the north 
differ in the feature characterization in comparison to those in 
the south. This leads to misclassified points, if parameters are 
trained on the southern part and tested on the northern one. 
However, the strong smoothing effect based on the increased 
neighbourhood for the CRF approach with N=4 helps 
increasing the results (Fig. 5c). This effect is caused by the 
interaction potential, which is basically a smoothing term. For 
water areas both correctness (52% vs. 66%) and completeness 
(41% vs. 82%) can be significantly improved compared to the 
Maximum Likelihood classification. 
For the mussel bed detection a low correctness and, in 
particular, completeness rate is obtained. The main reasons are 
that only few mussel bed regions are presented in the test site in 
comparison to the mudflat areas. Therefore, the numbers of 
samples of available data for training and testing is limited. 
Moreover, mussel bed and mudflat are characterized by similar 
features in some parts of the test site. Most of the significant 
features for the mussel bed detection rely on the relative 
elevation differences as well as on the curvatures of the surface. 
These features occur very similar near to tideways and lead to 
misclassification in these parts. Comparing the completeness 
(47%), the best is achieved using the CRF method with small 
  
  
Classes Mudflat Water Mussel 
bed 
ML Completeness | 97.7 % 41.0 % 17.6 % 
  
Correctness 94.3 % 51.8% 53.6 % 
  
  
CRF Completeness 98.5 % 51.6 % 46.8 % 
(N=2) | Correctness 97.7 % 07% | 426% 
  
  
  
CRF Completeness 90.8 % 82.4 % 56.5 % 
(N=4) | Correctness 98.8 % 66.3 % 8.5 % 
  
  
  
  
  
  
  
  
Table 1: Classification results with rates for Completeness and 
Correctness for the Maximum Likelihood (ML) Classification 
and the CRF method for varying neighbourhood N 
  
Figure 5: Classified point clouds obtained by Maximum 
Likelihood Classification (a) and the classification with our 
CRF approach for varying neighbourhood of N-2 (b) and N-4 
(c) 
neighbourhood (N=2). Nonetheless, the incorporation of 
context for a big neighbourhood (N=4) leads to an over- 
smoothing effect (Fig. 5b). Thus, the correction rate is very low 
because small mussel bed areas are misclassified and false 
positive points (mudflat points classified as mussel bed) occur 
on the border of the test site. The application of the Maximum 
Likelihood Classification leads to noisy appearance of the 
results (Fig. 5a). By incorporating context in the CRF method 
this effect can be avoided and the completeness rate can be 
significantly improved. 
5. CONCLUSION AND OUTLOOK 
In this paper we proposed a classification method for LiDAR 
data based on CRFs. We integrated contextual knowledge in a 
supervised classification process for LiDAR data in Wadden 
Sea areas. For this task we presented suitable classification 
features and learnt typical structures of the data in a training 
step. As result of the classification process, each point of the 3D 
point cloud is assigned to one of the three classes water, 
mudflat, and mussel bed. We tested different values for the 
neighbourhood in the CRF approach and compared the results 
to a  non-contexual method (Maximum Likelihood 
Classification). A test showed that the detection of water and 
mussel bed in LiDAR data is a challenging task. For water