Modulation Transfer Function measurement using non specific views

The measurement of the Modulation Transfer Function (MTF) to quantify the quality of an imaging system proves to be very important in the context of Earth observation satellites. hi particular, this measurement is essential to carry out the focusing of the telescope, or to implement a deconvolution filter whose goal is to enhance the image contrast or to reduce the noise. Its knowledge also allows us to compare the characteristics of different known and unknown satellites. In this paper, we suggest an univariant MTF measurement method using non specific views. First of all, the landscape has to be characterized in order to discriminate ground structure information from MTF information. Once this separation is carried out, landscape structure information can be extracted, allowing a classification between very uniform scenes and more structured ones. Then the MTF which is described by a bidimensional analytical physical model, can be assessed using an artificial neural network. The principle is to use the artificial neural network to learn the MTF of simulated or perfectly known images, and then to use it to assess the MTF of totally unknown images. One can show that this method is robust even if the noise is taken into account. As a result, maximum MTF assessment errors are less than 10%. This enables us to suggest further developments including a general scheme of criteria assessment of image quality.