Rapid-revisit Synthetic Aperture Radar (SAR) satellites in Low Earth Orbit (LEO) are promising with their observational persistence, low cost, and breakthrough engineering. However, the complexity of the backscattered SAR signal is quick to overwhelm both novice and expert data scientists.
At ICEYE Analytics, we are keen on building AI/ML applications for heavy-duty image processing and scalable analytics. The European Space Agency (ESA) Φ-lab presented us with an opportunity to lower the entry barrier to SAR-based ML applications. Due to the combination of our internal objectives and this fantastic ESA opportunity, we’ve begun to develop an open source Python library to easily enable ML exploration of ICEYE SAR images.
The ICEcube array handling library is our first step to lower the SAR barrier to entry. This library abstracts, yet persists, some of the metadata handling and optimizes array manipulation and memory usage through xarray data structures.