Automatic Extraction of Ridge Lines from Digital Elevation Models

Automatic Extraction of Ridge Lines from Digital Elevation Models

van Noppen, Thirza (TU Delft Civil Engineering and Geosciences)

van der Ent, R.J. (mentor)
Aguilar Lopez, J.P. (mentor)
Rutten, M.M. (mentor)
de Graaff, B.J.A. (mentor)
Donchyts, G. (mentor)
van Dam, A. (mentor)

Delft University of Technology

Civil Engineering | Hydraulic Engineering

TKI project: 'D-HYDRO Suite 1D2D ontwikkelingen'

2022-02-17

Second-order Gaussian kernels have been utilized to develop three algorithms that could automatically extract ridge lines for hydrodynamic modelling. Isotropic second-order Gaussian kernels produce inaccurate lines at crossings and junctions. To avoid the malfunctioning of Second-order Gaussian kernels, one default and two alternative algorithms were developed. The first, default algorithm is based on isotropic kernels and non-maximum suppression. For the first alternative algorithm, isotropic and anisotropic kernels have been applied for the filter process. The third algorithm uses skeletonization instead of non-maximum suppression. A verification was applied to analyzed the performance of the algorithms. The Matthews correlation coefficient (MCC) of the default algorithm and the alternative algorithm that included anisotropic kernels was found to be 0.17. For the algorithm based on skeletonization a value of 0.08 was obtained. Hence it has been concluded that the algorithms that utilized non maximum suppression instead could more accurately detect ridge lines than the model based on skeletonization. However, the latter generated lines that contained less discontinuities. Furthermore this algorithm turned out to be computationally less demanding in comparison to the other two algorithms.

image processing
Ridge detection
DEM-analysis
Second-order Gaussian kernels
Filtering
Hydrodynamic modelling
1D-2D model
D-Hydro
De Roer
Kernel convolution
Limburg
Inundation
Sub-grid modelling

http://resolver.tudelft.nl/uuid:ab59f7b6-ba98-4717-991b-18b2c9143f78

Student theses

master thesis

© 2022 Thirza van Noppen