## 1 Introduction
### Related Work
For each feature, a membership value (between 0 and 1) was assigned to each LiDAR point, where the values \"0\" and \"1\" indicate the mudflat and water class, respectively. Two study areas were tested, and the results yielded a completeness and correctness value up to 99.2% and 98.5%, respectively. [PERSON] et al. [21] introduced a point-wise supervised classification method based on geometric and intensity features. Eight features were extracted and used to build a classifier based on conditional random fields to distinguish between water, mudflat, and mussel bed classes at the Wadden Sea. The water class was classified with a completeness of 82.4% and a correctness of 66.3%. [PERSON] et al. [19] proposed a workflow for water surface classification and land/water boundary delineation based on a region growing algorithm. The seed points were first selected based on a minimum intensity density threshold value. The growing criteria were conducted by applying threshold values on the height difference, intensity density, and intensity variation in order to create segments. All segments were then classified into land and water segments by applying a threshold on minimum segment size and mean segment roughness. The algorithm demonstrated an overall classification accuracy of 97% with a root mean square error of 0.45 m for the land/water boundaries. Despite achieving a higher overall accuracy, this method requires a significant pre-processing step, which is the model of LiDAR data dropouts. This step cannot be conducted without the knowledge of the pulse repetition frequency, Global Positioning System (GPS) timestamps, and the scan direction. In addition, many threshold values were manually selected to fit the tested data. Nevertheless, this method could not discriminate water surface from asphalt because they both exhibited low intensity and smooth surface. [PERSON] et al. [22] delineated wet channels using LiDAR intensity and elevation data. The vegetation canopy was first filtered out by applying a threshold value on elevations, and a digital elevation model (DEM) was created. Potential wet channels extents were then extracted based on their geomorphic characteristics. In parallel, an intensity image was created from ground returns, and a Gaussian mixture model was used to decompose the intensity image into three classes, namely wet, transition and dry classes. The edges of wet channels were then extracted from the gradient of the intensity image. Finally, the networks, extracted from the LiDAR DEM and the intensity image were overlaid. However, the final network was disconnected because of the mismatch between the two networks. This is mainly due to distortion of the DEM through the generation process or error in the DEM itself. Another possible reason for disconnection is that the low quality of data and missing intensity data. The supervised machine learning techniques such as SVM [17], decision trees [18], or random forest are attributed to the amount of available field sample data. These samples are used as training data, which are usually limited, and affect the achieved accuracy by those methods [23]. In addition, the output results of those methods are produced using data-driven threshold values, which cannot be generalized for any dataset under different conditions. We aim in this research to present an unsupervised and fully automated land/water discrimination method using LiDAR data acquired at different wavelengths. In addition, we use the seeded region growing algorithm that considers the spatial coherence of the LiDAR data by using the neighboring points of each LiDAR point.