Calving events detection and quantification from time-lapse images in Tunabreen glacier

An automatic observation method for calving activity is an absolute necessity for researchers to collect statistical data for deeper understanding of the activity that is well known as a contributing factor for sea level rise. In this paper a new framework for calving event detection and area estimation is presented. First, a set of time-lapse images are registered where the first image in sequence acts as a reference for others. Registration process exploits M-estimator Sample Consensus (MSAC) to build transformation model based on matched Speeded-Up Robust Features (SURF) features. After that, terminus of glacier is extracted by a semi-automatic Chan-vese level-set segmentation. Then, calving regions in a terminus are recognized as Local Binary Pattern (LBP) texture difference of two consecutive images. Since the difference forms clustered points, a-shape reconstruction is applied to form polygons representing changed areas. Finally, the areas of changed regions are estimated by a pixel scaling technique. Experimental result on noise-free images confirms the effectiveness of the proposed framework.