Non-Gaussian Extensions for the Detection of Persistent Scatterers: Addressing the Limitations of Gaussian Models for InSAR Imagery

It is well-known that the backscatter of high-resolution Synthetic Aperture Radar (SAR) imagery is non-Gaussian in nature. As a result, corresponding heavy-tailed models have been successfully incorporated for the design of improved SAR target detectors. However, Gaussian-based detectors are largely still applied for selection of persistent scatterers (PS) in Interferometric Synthetic Aperture Radar (InSAR) imagery, and implications for the performance of PS techniques have not been well-studied. Here, we extend an existing Gaussian model for PS to incorporate non-Gaussian behavior. We then implement the model for PS detection and compare its performance to its Gaussian counterpart, finding that the non-Gaussian model finds a slightly denser network of PS. Further work will focus on analyzing the characteristics of this disparity, including its relationship with terrain and system parameters such as wavelength and bandwidth, and compare the estimated deformation from the non-Gaussian detector compared to an existing Gaussian-based model. Understanding the limitations of Gaussian models will inform the design of improved PS detectors to produce more complete deformation maps and enable the broader application of InSAR for challenging applications, such as observing small strain rates in natural terrain.