I've got a hobby-interest in remote sensing (satellite imagery). Over the past couple of days, I've been playing around with data from the ESA's Sentinel-1 mission. The ESA (being cool and European Union-y) makes most of the data from Sentinel series of satellites freely accessible to the public, and provides some decent software for processing and analysing the data.

Sentinel-1 is a synthetic aperture radar (SAR) satellite. I don't fully understand the physics behind SAR, but it's basically an active radar measurement of the ground track the satellite passes over. Different surfaces give different sorts of radar returns (measured as a change in polarisation), and so SAR can be used to classify different terrains (crops, forests, grasslands, rock, etc), like in the false-colour image of Flevoland I've attached. Resolution is moderate: for Sentinel-1, each pixel ends up being about 4x4 m on the ground.

SAR imagery does not have amazing spatial resolution, but is often good enough to do things like identify shipping. Water is a uniquely flat surface, so metal objects floating on water give a good return against a low background signal*. Some computationally demanding image processing later, and you can pick out ship locations. I've got a vague idea that it could be interesting to find ships in the territorial waters of North Korea, correlate against AIS tracks, and try to find some sanction-busting shipping running dark without AIS.

*This makes me wonder: the USSR really struggled with power requirements for the radar on its RORSAT ocean-monitoring satellites, to the point that it ended up having to power them using the only nuclear reactors to be launched into space. Why is SAR so much more efficient?

@spinflip could it be advances in sensors? All things being equal though, SAR combines multiple scans of the same area so things with a snr of higher than the background will tend to stand out more since some of the background noise will cancel out as you combine scans.