Tittel på avhandlingen: Multitemporal Analysis of Multipolarization Synthetic Aperture Radar Images for Robust Surface Change Detection
The thesis includes two distinct approaches for change detection from multidimensional time series of radar images: a post-classification comparison algorithm and a direct change detection algorithm. In the first approach, we consider the complete workflow associated with performing post-classification change detection from time series of polarimetric radar images for glacier change detection. The classified images of succeeding years are compared, and temporal changes are identified in the location of boundaries between glacier zones. In the second approach, two co-registered and co-calibrated polarimetric radar images are compared directly on a pixel-by-pixel basis by a novel test statistic for direct change detection. Finally an an appropriate threshold should discriminate changed and unchanged areas. The performance of the proposed method is demonstrated with good results on simulated and real data sets.
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