Automatic Channel Network Extraction from Remotely Sensed Images by Singularity Analysis
This work addresses the need for easier classification and analysis of channel networks in river studies, offering a domain-specific tool for fluvial geomorphology.
The paper tackles the problem of extracting river channel networks from remotely sensed images by proposing an automated method based on the Multiscale Singularity Index, which detects curvilinear structures and estimates channel widths, providing a robust alternative to existing remote sensing procedures.
Quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose a new method that extracts channels from remotely sensed images and estimates their widths. Our fully automated method is based on a recently proposed Multiscale Singularity Index that responds strongly to curvilinear structures but weakly to edges. The algorithm produces a channel map, using a single image where water and non-water pixels have contrast, such as a Landsat near-infrared band image or a water index defined on multiple bands. The proposed method provides a robust alternative to the procedures that are used in remote sensing of fluvial geomorphology and makes classification and analysis of channel networks easier. The source code of the algorithm is available at: http://live.ece.utexas.edu/research/cne/.