We do not currently use SAR! High-resolution SAR is currently very hard to find. Vendors like Capella Space are pretty pricey for HADR operations, though they do provide some data for deeply discounted/free prices for major disasters.
Beyond cost, SAR is just a deeply different form of imaging. The underlying physics that define a SAR image are so different from EO that many computer vision methods, in my opinion, will need to be re-invented to accommodate it. In fact, the next good bit of my Ph.D. is dedicated to accomplishing just that.
Urban scenes look very chaotic in SAR due to multi-path and layover effects. Defining damage in that environment is more aggregate than individual buildings. We're trying to figure out a good way to define what damage even means on SAR. In fact, one of the biggest accomplishments of our xView2 work was being the first comprehensive effort to define what damage means, for any natural disaster, from EO satellite imagery!
Thanks for the writeup! Agree on SAR being vastly different to interpret, and I can imagine the priciness. I also understand that the capacity/demand is another complicating factor now over Ukraine.
As you explained SAR phenomenology is not straight forward at all (pun intended). If I could suggest an option to explore a SAR-based damage proxy indicator, it would be interferometric coherence. This suggests however that you get your hands on InSAR data, both timely (to avoid decorrelation, which can happen over the span of a few days) and at good resolution to get relevant information at building-level.
As you can imagine this is not easy nor cheap to get. I am unaware if Capella is InSAR-capable; COSMO-SkyMed and TerraSAR/TanDEM/PAZ are the usual suspects when it comes to SAR constellations with InSAR capability and good frozen orbits. ICEYE I believe is trying to get there, but things are evolving rapidly. Sentinel-1 is a wonderful mission with solid InSAR capability, but I am not sure C-band delivers the resolution you need. Maybe NISAR is worth keeping an eye on in the future?
Sentinel-1 is the current workhorse for all sorts of InSAR things, but as you mentioned, the resolution is pretty meh for things like damage assessment. Beyond InSAR, people have been doing change detection on GRDs to get damage proxy maps, but that provides areal information rather than instance.
An opportunity in this space is to build truly complex-valued neural networks to fully exploit SLCs and other phase-based information.
Thanks for thinking well of the work! We've been scaling out impact with AI + SAR over the last year. Some work you may be interested in is our work on detecting dark vessels from SAR which has led to the interdiction of illegal fishermen and human traffickers! [0][1]
Beyond cost, SAR is just a deeply different form of imaging. The underlying physics that define a SAR image are so different from EO that many computer vision methods, in my opinion, will need to be re-invented to accommodate it. In fact, the next good bit of my Ph.D. is dedicated to accomplishing just that.
Urban scenes look very chaotic in SAR due to multi-path and layover effects. Defining damage in that environment is more aggregate than individual buildings. We're trying to figure out a good way to define what damage even means on SAR. In fact, one of the biggest accomplishments of our xView2 work was being the first comprehensive effort to define what damage means, for any natural disaster, from EO satellite imagery!