Dependence of the recognition probability of an aerial reconnaissance object with linear resolution on the ground and pixel information content of the image

The paper considers a quantitative relationship between the probability of recognizing an aerial reconnaissance object, the actual linear resolution on the ground, and the pixel information content of a digital image. The objective is to obtain a computational model for assessing whether an object can be recognized at the levels of species, class, subclass, and type, taking into account the minimum number of informative pixels and the stability of recognition features. The digital portrait of an object is represented as a set of pixel configurations containing geometric, brightness, contrast, and structural features. The model includes the effective projection of the object, viewing angle, contrast, image smear, representativeness of features, and local resolution of characteristic details. A pixel-resolution indicator of recognition sufficiency is proposed. It expresses the ratio of the actual effective pixel information content of an image to the information content required for a specified classification level. Direct relationships are obtained for calculating recognition probability from a given linear resolution on the ground, and inverse relationships are obtained for determining the required resolution and the minimum number of pixels from a specified recognition probability. The model can be applied in planning aerial imaging, testing optoelectronic systems, and setting requirements for reference digital-portrait databases of observed objects.