A Clustering Based Automated Glacier Segmentation Scheme Using Digital Elevation Model

Abstract

We present an automated scheme for segmentation of high mountain glaciers using Fast Adaptive Medoid Shift (FAMS) algorithm and Digital Elevation Model (DEM). FAMS is a non-parametric clustering technique that has been optimized and made data driven from its original Medoid Shift algorithm. 6 Band TM sensor satellite images are fed to FAMS as input along with height, slope and gradient information extracted from a DEM. Clean glacier and debris covered glacier are treated separately. Each glacier having its own regional minima and debris is delineated individually. A unique slope-gradient model is used to separate the debris covered portion from its surrounding and extension rocks as well as to exclude the lateral moraine. The proposed model is independent of the DN values of satellite image bands and therefore is able to perform well even in areas where debris covered glaciers exactly resemble the surrounding rocks. Experiments have been carried out on KaraKoram and Hindukush mountain ranges of Asia and validated against supervised manual segmentation results as well as Google EarthTM imagery. Results have shown our fully automated method to be time efficient, robust and accurate.