28 January 2002 Signal-to-noise ratio assessment from nonspecific views
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Abstract
The signal to noise ratio is one of parameters checked in flight to assess the image quality of remote sensing satellites. A simple method to estimate this parameter consists in selecting huge snowy areas. As the landscape is nearly uniform, a correct estimation of the standard deviation of the noise can be done by calculating the standard deviation of the signal. In order to avoid viewing such specific scenes, we suggest two different approaches. The first one is restricted to additive noises. As there is little correlation between the noise and the landscape, images can be decomposed in an image considered as pure landscape and an image of noise where the signal to noise ratio is estimated by using a block computation method. Different simulations show that the assessment errors are less than 10% and usually near 5%. The second one is a particular application of a general approach of image quality assessment. It can be applied to any kind of noise model. It is based on artificial neural network use. The principle is to use artificial neural network to learn the signal to noise ratio of simulated or perfectly known images, then use it to assess the signal to noise ratio of unknown images. The assessment errors are near 10%.
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Jean-Marc Delvit, Dominique Leger, Sylvie Roques, Christophe Valorge, Francoise Viallefont-Robinet, "Signal-to-noise ratio assessment from nonspecific views", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); doi: 10.1117/12.454171; https://doi.org/10.1117/12.454171
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