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I have a series of satellite images in 4 bands (red, green, blue and NIR). What is the easiest way to estimate what percentage of each image is covered by clouds? The image is less than 1x1 km with 3-5 m resolution.
As far as I understand, fmask does not work here (it requires more bands), but maybe there is some simple heuristic (preferably one that can be calculated just on a pixel-by-pixel basis)?
There are heuristics for cloud detection but those depend on the exact wavelength of your bands and therefore on the sensor you are using. at 3-5m rgb+nir I would look for similar sensors and available cloud detection algorithms.
RapidEye (5m, rgb+red-edge+nir) comes pretty close and has available heuristics for cloud screening - e.g. from CONABIO - rapideye-cloud-detection.
There are other publications trying to solve the issue of cloud detection with a small amount of bands, however most of them incorporate the short wave infrared bands (e.g. Hollstein et al., Fisher).
The lack of infrared bands makes the cloud screening a challenging endeavour and recent trends are trying to incorporate deep learning to solve the issue, such as Planets Amazon from Space Kaggle Challenge.
