Automatic and high-precision extraction of rivers from remotely sensed images with Gaussian normalized water index
采用高斯归一化水体指数实现遥感影像河流的精确提取

The accurate extraction of rivers is important for survey of water resources, time series change detection on water usage, assessment of large-scale water conservancy facilities, and so on. The general methods of river extraction are difficult to be applied widely because of the disruption by clouds, snow, shadow of mountains, and lakes in remotely sensed images. In this paper, we propose a new index calculation model for river extraction, which is based on an improved water index, named Gaussian normalized difference water index (GNDWI). The model can remove the interference factors effectively by the aid of a DEM. The experiment for the extraction of Ili River from Landsat images show that the new model can automatically and rapidly extract the river in very complex environments. Furthermore, shadows and other useless information can also be effectively removed with a high accuracy.