Last years we reported at the SPIE conferences the results of development of a hierarchical structural classifier which used the contour structural elements as an input and was designed for matching the aerospace photographs taken in different seasons from different view points, or formed by different kinds of sensors. The aim of this investigation was development of a theoretical approach which could explain the previously described empirical results and could give a proof for the techniques applied in the elaborated algorithms, since many of these techniques were borrowed from the human vision system or were introduced heuristically. The proposed approach is based on the information theory and minimum description length principle (MDL). This principle can be stated in the following way. Such a model of the initial data should be chosen, which gives their shortest description without information losses when the chosen data model is extended with the description of discrepancy between the model and the data or with the description of the random component. In our case the data is a pair of images to be registered. In the task of image matching the images models are extended with the model of their mutual spatial transformation, and such the transformation is chosen which permits to minimize the joint description of a pair of images. To apply the MDL principle the model is introduced which formalizes the image structural description used in the classifier. Consequently, the methods developed earlier were reformulated in the terms of the proposed theoretical approach. As a result, the necessary improvements of the structural classifier were determined which can increase its reliability.