Online Learning and Decision Making for Real-Time Analytics of Remote-Sensing Data

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. The conventional methods analyze the existing batch of radar data in an offline manner; nonetheless, with the rise of new technologies, the amount of data becomes massive in a short time, which renders offline analytics dramatically inefficient. Real-time methods, in contrast, significantly enhance the effectiveness and efficiency of interferometric SAR (InSAR) time-series analysis.  

Such methods are particularly useful in delay-sensitive and resource-constrained applications such as early detection of geo-hazards; Nevertheless, online analysis imposes some challenges. Specifically, it becomes imperative to develop low-complexity, easy-to-implement algorithmic solutions that adapt to the data arrivals on-the-fly. 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 that represent the entire set from some specific perspective. Moreover, while analyzing the data, it is crucial to consider the temporal correlation between the data points and the features. This project focuses on developing such solutions.

This project belongs to the framework of the HEIBRIDS Graduate School of Data Science. It is performed in cooperation with Prof. Mahdi Motagh at the German Research Center for Geosciences, Helmholtz Institute Potsdam. The responsible researcher for this project is Mr. Binayak Ghosh.