519.2 Simulation of locally homogeneous radar images using different statistical criteria

Dostavalova A. M. (Bauman Moscow State Technical University)

CLASSIFICATION OF RADAR IMAGES, BAYESIAN METHOD, DISCRETE MIXTURE OF DISTRIBUTIONS, TESTING THE HYPOTHESIS ABOUT DISTRIBUTION TYPE, KOLMOGOROV'S TEST, CRAMER-MISES-SMIRNOV'S TEST, POWER OF THE TEST


doi: 10.18698/2309-3684-2021-4-103120


The article deals with the problem of classifying pixels of the radar image (RI). A locally homogeneous radar image model was used, in which the readings of each small area (local area) were considered to belong to only one class. The classification results of several real radar images by local areas are compared using the statistical criteria for the maximum a posteriori probability, Kolmogorov and Cramer-Mises-Smirnov. At the same time, in the case when the listed criteria made it difficult to classify a local area — when it hit the interface of the underlying surfaces, it was considered to be assigned to a special, boundary class, and its readings were processed using the grid method for separating mixtures of probability distributions. For each criterion, the classification accuracy was evaluated as the proportion of correctly classified pixels within the selected homogeneous areas. It has been established that in the case of significant interclass differences, the best classification accuracy is ensured by the use of the least powerful Kolmogorov criterion among nonparametric criteria. Also, using a real image as an example, it is shown that when the differences in the characteristics of objects of the same class are comparable to interclass differences, the highest classification accuracy is achieved when using the maximum a posteriori probability criterion. Such cases are typical for a wide class of classification problems, including those not related to image processing.


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