| Summary: | A fully automated method for detecting and measuring calving regions of a glacier is an important tool to gather massive statistical data of calving events. A new framework to achieve the goals is presented in this thesis. First, time-lapse images are registered to the first image in the set. Registration process makes use of the M-Estimator Sample Consensus (MSAC) method to estimate a transformation model that relates a pair of Speeded-Up Robust Features (SURF). Then, the terminus of a glacier is separated from other objects by a semi-automatic Chan-vese level-set segmentation. After that, calving regions in a terminus are detected as a combined difference of Local Binary Patterns (LBP) of two successive images. Clustered points that form a difference image are transformed into polygons representing changed regions by applying the α-shape method. Finally, the areas of changed regions are estimated by the pixel scaling method. The results highlight the performance of the method under normal conditions and reveal the impact of various weather conditions to the performance of the method.
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