CLASSIFICATION OF AIRBORNE LASER SCANNING DATA IN WADDEN SEA 
AREAS USING CONDITIONAL RANDOM FIELDS 
    
  
  
A. Schmidt, F. Rottensteiner, U. Sörgel 
Institute of Photogrammetry and GeoInformation 
University of Hanover 
{alena.schmidt, rottensteiner, soergel } @ipi.uni-hannover.de 
Commission III - WG 2 
KEY WORDS: 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. 
1. INTRODUCTION 
Due to its efficient way of three dimensional data generation, 
airborne laser scanning, also called LiDAR (Light Detection 
and Ranging), has become a standard method for recording 
topographic data. In coastal areas one major application arises 
in the field of waterway and coast protection. In the framework 
of a German research project (WIMO, 2012) our focus in this 
field of LiDAR applications is on monitoring of the Wadden 
Sea, a unique habitat in the southeaster part of the North Sea. 
Due to its biological diversity, the German part of the Wadden 
Sea is among UNESCO's World Heritage List. However, it is 
influenced by climate change and human activities. For these 
reasons a recurrent monitoring of these areas becomes 
necessary. Monitoring involves the classification of LiDAR 
data, which is necessary for two reasons. 
Firstly, tidal flows, storms, climate change, and human 
activities cause morphological changes of various kind. The 
morphology of the terrain can be represented by digital terrain 
models (DTM). Highly accurate height data are obtained by 
LiDAR. In tidal trenches, where residual water remains even 
during low tide, data acquisition by laser scanning is limited to 
the water surface, because the near-infrared laser pulses can not 
penetrate water. Therefore, a height model generated from laser 
scanner point clouds over water regions does not represent the 
actual terrain level underneath. The generation of a DTM thus 
requires the detection of water surfaces, which leads to the first 
crucial classification into land and water areas. Such a 
classification having been carried out, an additional data source, 
e.g. sonar, can be used to complete the DTM in the water areas. 
Secondly, for the Wadden Sea monitoring the analysis of 
biodiversity and mapping of habitats is of great interest. This 
leads to a separation of the class land into different subclasses. 
Whereas this has been shown to be possible with spectral 
information from remote sensing image data (Klonus, 2011), 
such a classification based on monochromatic LIDAR data has 
not yet been investigated. Due to the lack of spectral features, 
the distinction between the habitats based on LiDAR is a 
difficult task. On the other hand, besides the purely geometric 
measurement of 3D coordinates modern LiDAR systems record 
also the intensity of the backscatter, which can provide 
information about additional target characteristics like 
roughness. Given the properties of LiDAR, only habitats 
characterised by their surface roughness, e.g. mussel beds, can 
be expected to be distinguished. Thus, we differentiate two 
subclasses of land, namely mudflat and mussel bed. 
Our aim is to classify the LIDAR data by assigning a class label 
to each point in the point cloud. We distinguish the three classes 
water, mudflat, and mussel bed. Because of the rather 
homogeneous appearance of the Wadden Sea, which mainly 
consists of flat areas with hardly any discriminative objects, the 
classification becomes a challenging task. Therefore, we need 
good classification features as well as a powerful classification 
approach. A flexible classification method is provided by the 
Conditional Random Field (CRF) framework. The advantage of 
this approach is the incorporation of contextual knowledge into 
the classification process. 
In our previous work on the detection of water areas (Schmidt et 
al., 2011) we have shown that the completeness rate for wafer 
was limited due to the fact that no context was considered in the 
classification process. In this paper, we want to present how 
these problems can be overcome by the use of CRFs. We focus 
on the implementation of a CRF framework for LiDAR data and 
on the extraction of optimal features for our specific 
classification task. 
1.1 Related Work 
Whereas there are many approaches dealing with the 
classification of LIDAR data for the detection of objects such as 
buildings or vegetation, there are only a few studies on the 
classification of water surfaces, in particular in Wadden Sea 
areas. One exception is Brzank (2008), who presents a 
classification method based on fuzzy logic as a first step