Space-borne SAR interferometry: Three Decades of Innovation and Problem Solving

Wednesday, October 24, 2018 - 11:10am - 12:00pm
Keller 3-180
Manoochehr Shirzaei (Arizona State University)
With the global population surpassing 7.6 billion people in 2018, the impacts of human activities on the environment are noticeable almost everywhere on our planet. The consequences of these impacts are still elusive, particularly when trying to quantify them at larger scales. It is essential to trace environmental changes from a local to global scale over several decades. This task is increasingly fulfilled by Earth observation (EO) satellites, in particular, radar imaging instruments. Synthetic Aperture Radar (SAR), a cloud-penetrant microwave imaging system, provides unparalleled day-night and all-weather monitoring capabilities. Availability of repeated SAR acquisitions with similar imaging geometry allows performing interferometric SAR (InSAR) processing. InSAR uses radar to illuminate an area of the Earth’s surface and measures the change in distance between satellite and ground surface, as well as the returned signal strength. Such measurements are
suitable for generating high-resolution digital elevation models and accurate terrain deformation maps.

While many multi-decadal EO datasets are already available, the major limitation hindering their effective exploitation in global change studies is the scarcity of tools and algorithms required for high quality and efficient data processing at a fine spatial resolution. From a scientific perspective, there is a great demand to build up science-driven computational platforms that are transparent for their users and are diverse and flexible regarding the datasets and algorithms used. From a commercial perspective, timely access to precise measurements of the land surface change at high accuracy and resolution is vital. From a security perspective, an all-weather continuous monitoring system is ideal for various purposes such as object tracking and change detection.

Firstly, I will review some of the recent advances in applying wavelet transforms and Kaman Filter to perform multitemporal InSAR processing on large datasets. Next, I will discuss applications of innovative statistical approaches such as Bayesian analysis for semi-real-time change detection and image classification based upon SAR acquisitions. At last, I will discuss some of the innovative applications of stochastic and heuristic optimization algorithms for inverting InSAR derived deformation maps to constrain the underlying processes associated with tectonic, volcanic and hydrological phenomena.