SAR & INSAR
Sensor-agnostic, highly accurate and automated algorithms that support common Synthetic Aperture Radar (SAR) imagery and workflows
A New Era for SAR
Embrace the exciting rebirth of Synthetic-Aperture Radar (SAR) based earth observation with powerful algorithms that have been developed by leading experts. With so many free and commercial constellations already providing terabytes of data on a daily basis, the opportunities to conduct science and develop services have never been better. Leverage the power of the CATALYST platform to implement automated SAR workflows.
SAR Interferometry (InSAR)
SAR Interferometry is a proven technique to derive valuable information for ground displacement applications. The CATALYST platform offers flexibility to complete InSAR processing steps manually, or through full automation. Multiple approaches can be addressed including Differential InSAR (DInSAR), Small BAseline Subset (SBAS), and Persistent Scatterer Interferometry (PSI).
InSAR analysis is often limited in terms of geographic coverage due to loss of coherence in areas with vegetation or rapid changes. Using persistent target detection across temporal stacks, individual points can be extracted to infer displacement.
SAR Object Analysis
Object Based Image Analysis (OBIA), a segmentation-based approach to image classification, can greatly benefit from fusing SAR and Optical imagery. The CATALYST platform leverages rigorous algorithms to best pre-process SAR imagery and improve the signal-to-noise ratio. For single, dual, quad, or compact polarized data, derived statistics can be extracted from either the intensity or the phase-and-magnitude elements of the signal (e.g. polarimetric decompositions), thus providing more information to achieve class separation and deriving finer details.Object Based Image Analysis (OBIA), a segmentation-based approach to image classification, can greatly benefit from fusing SAR and Optical imagery. The CATALYST platform leverages rigorous algorithms to best pre-process SAR imagery and improve the signal-to-noise ratio.
For single, dual, quad, or compact polarized data, derived statistics can be extracted from either the intensity or the phase-and-magnitude elements of the signal (e.g. polarimetric decompositions), thus providing more information to achieve class separation and deriving finer details.