These are notes from a one-day project to test a hunch. The idea is to train a convolutional neural network to remove speckle from sar (synthetic aperture radar) using only one other observation – with its own speckles – as the target. This method does not come close to state of the art despeckling, and can be biased by the skewed distribution of noise in a way that makes it useless for quantitative research. However, I hadn’t noticed it in the literature and I think it’s kind of funny, so I’m writing it up.
Everything here is about Sentinel-1 L1 GRD-HD data, since it’s what I used, since it’s free.
Sar observations contain speckle, a form of interference related to the sparkles in reflected laser light. By some definitions speckle is not noise, since it’s physically real outside the sensor and contains information, but we will treat it as noise. Speckle is (close enough to) independent between radar chirps, a.k.a. looks, and even its distributio
