Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis
The quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose anew 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 strongly responds to curvilinear structures but weakly responds to edges. The algorithm produces a channel map using a single image where water and nonwater 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 the remote sensing of fluvial geomorphology and makes the classification and analysis of channel networks easier. The source code of the algorithm is available at http://live.ece.utexas. edu/research/cne/.