Automatic Channel Network Extraction from Remotely Sensed Images by Singularity Analysis

TL;DR

This paper introduces an automated method for extracting and analyzing river channel networks from remote sensing images using singularity analysis, aiding geomorphological studies.

Contribution

It presents a novel, fully automated approach based on Multiscale Singularity Index for extracting channels and estimating their widths from single remote sensing images.

Findings

Robust channel extraction from various remote sensing images.

Effective estimation of river widths.

Simplifies classification and analysis of channel networks.

Abstract

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/.