  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/.
