Get HyP3! Cloud-native SAR Processing
Abstract
Sentinel-1 Synthetic Aperture Radar (SAR) data is a freely available global dataset, making it an ideal candidate for interferometry and as a dataset for change and event monitoring. Continuous data acquisition, independent of cloud conditions or light availability, makes SAR invaluable for monitoring and informing decision making on natural and anthropogenic hazards, but processing SAR data for analysis-ready products is complex, resource-intensive and requires specialized software.
The Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) is a high throughput computing (HTC) platform that provides cloud-native processing of Sentinel-1 SAR data at no cost to users. Computing is done in parallel for rapid product generation, easily producing hundreds to thousands of products an hour. HyP3 is directly integrated into Vertex, ASF's primary data discovery tool, where users can easily select an area of interest on the Earth, find available Sentinel-1 products, and submit jobs with a click of a button to HyP3 for Radiometric Terrain Correction (RTC), Interferometric SAR (InSAR), and/or other forms of processing. Access to HyP3 can also be built into user workflows programmatically via a RESTful API or a python software developers kit (SDK). Each submission allows customization of processing options and returns metadata-rich, analysis-ready final products to users. HyP3 is an open source platform and openly developed for use in the Amazon Web Service (AWS) cloud. Designed to have minimal overhead costs (serverless design), be easily deployable using cloud-formation templates (infrastructure as code), and to allow scientists and users to develop new processing plugins, HyP3 has increasingly been leveraged for project (grant) specific processing. With project-based custom HyP3 deployments, science teams can quickly and easily develop new algorithms/products, control processing/product access, provide project-based cost accounting, and leverage AWS cloud credits provided by funding agencies, all without needing to be cloud architects/engineers.- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMIN42C0343S