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After launch and during continuous radiation exposure, space-borne cameras are constantly changing. Therefore, permanent technical determination and evaluation of the sensor in space plays an important role in the remote sensing community. There are a variety of evaluation criteria, which are all based on the essential camera parameters - the spatial resolution, point spread function (PSF) and noise.

Noise estimation is a challenging task for characterization of remote sensing systems in space. The in-flight measurement of noise will often be done with artificial test sites. If these test sites are not sufficiently available, homogeneous image regions (desert, snow, water surfaces) are often used. The albedo of these regions, however, lies normally outside the specified albedo range of remote sensing systems focused on agriculture and forestry areas. The only possibility to determine the noise after the satellite launch within the specified operational albedo range is to use very small image areas with the required albedo within the acquired imagery. As these objects have to be homogeneous, one needs methods that can detect the smallest homogeneous areas in the image to evaluate noise.

In this paper an approach for determining the signal-to-noise ratio (SNR) with data from natural targets is presented. In experiments, the results demonstrate that the described method performs quite satisfactorily and results are comparable to the standard methods used to determine SNR.