River segmentation using satellite image contextual information and Bayesian classifier

Abstract

Satellite-based remote sensing imaging can provide continuous snapshots of the Earth’s surface over long periods. River extraction from remote sensing images is useful for the comprehensive study of dynamic changes of rivers over large areas. This paper presents a new method of extracting rivers by using training samples based on the mathematical morphology, Bayesian classifier and a dynamic alteration filter. The use of a training map from erosion morphology helps to extract the non-predictive river’s curves in the image. The algorithm has two phases: creating the profile to separate river area via evaluated morphological erosion and dilation, namely, a training map; and improving the river’s image segmentation using the Bayesian rule algorithm in which two consecutive filters swipe false positive (non-water area) along the image. The proposed algorithm was tested on the Kuala Terengganu district, Malaysia, an area that includes a river, a bridge, dam and a fair amount of vegetation. The results were compared with two standard methods based on visual perception and on peak signal-to-noise ratio, respectively. The novelty of this approach is the definition of the contextual information filtering technique, which provides an accurate extraction of river segmentation from satellite images.