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. In 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. We suggest an univariate MTF measurement method using nonspecific views. First of all, the landscape has to be characterized 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 use it to assess the MTF of totally unknown images. We show that this method is robust even if 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 for assessment of image quality.