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TU Berlin

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Remote Sensing Data Analysis

Enabled by recent technological advances, the field of radar remote sensing has entered the era of Synthetic Aperture Radar (SAR) missions with short revisit times, providing an unprecedented wealth of topography and surface change time-series. In conventional data analytics, the analytics is performed on an existing batch of data in an offline manner; nonetheless, by the rise of new technologies, the amount of data becomes massive in a short time, which renders offline analytics quite inefficient. Resorting to real-time analytics promotes effectiveness and efficiency of interferometric SAR (InSAR) time-series analysis, particularly in delay-sensitive and resource-constrained applications, for example, early detection of geo-hazards; nevertheless, online analysis imposes some challenges. Specifically, it becomes imperative to develop low-complexity, easy-to-implement algorithmic solutions which adapt to the data arrivals on-the-fly. In order to manage the huge volume of InSAR point clouds that are obtained by multi-temporal interferometric time-series approaches, one solution is to summarize the large data sets by extracting the most important data points which, to a great extent, represent the entire set at least from some specific perspective. Moreover, while analyzing the data, the temporal correlation between the data points and/or the features shall be taken into account.

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