Enabled by recent technological advances, the field of radar remote sensing has entered the era of explosively-growing wide-swath Synthetic Aperture Radar (SAR) missions with short revisit times (1-6 days), such as Sentinel-1 and the planned Tandem-L and NISAR missions, providing an unprecedented wealth of topography and surface change time-series using interferometric SAR (InSAR) technique. Such data volume can be characterized with (i) huge volume and large variety; (ii) complexity and high dimension; (iii) partial unreliability; and (iv) correlation or similarity. Thus, for the retrieval of geophysical signal from InSAR time-series, the data should be classified, clustered and cleaned, performed by data analytics, to reliably detect anomaly changes and improve susceptibility in the areas affected by deformation due to natural and manmade hazards. In the state of- the-art literature for exploiting InSAR time-series data, the separation of geophysical signals from noise artifacts such as atmosphere and decorrelation can be divided into two main parts: (i) data processing for efficient estimation of the phase considering long stack of the data; and (ii) data analysis and decision. In brief, the main goal of this project is to develop a generic framework for real-time InSAR data analytics using the theory and methods from online machine learning and sequential decision making, and to design efficient algorithmic solutions for the retrieval of geophysical signals from SAR measurement. In particular, the concentration is on SAR data from Sentinel-1 satellite. On the online learning and classification side, the methodology is concentrated on online machine learning algorithms. Specific attention is given to submodular optimization. On the online decision making side, the basic method is sequential optimization with limited feedback, especially multi-armed bandit.