MULTICHANNEL IMAGES POST-CLASSIFICATION PROCESSING TECHNIQUES ANALYSIS

2019

Радіоелектронні і комп'ютерні системи, Vol 0, Iss 1, Pp 17-28 (2019)

The subject matter of the article is the methods of morphological spatial filtering of images in pseudo-colors obtained as a result of statistical segmentation of multichannel satellite images. The aim is to study the effectiveness of various methods of post-classification image processing in order to increase the probability of correct recognition for observed objects. The tasks to be solved are: to select a mathematical model describing the training sets of objects’ classes; to implement the procedure of statistical controlled classification by the maximum likelihood method; to evaluate the results of objects’ recognition on the test image by the criterion of the empirical probability of correct recognition; to formalize the procedures of local object-oriented filtering of a segmented image; to investigate the effectiveness of rank filtering as well as weighted median filtering procedures taking into account the results of the classification by k-nearest neighbors in the filter window. The methods used are methods of empirical distributions’ approximation, statistical recognition methods, methods of probability theory and mathematical statistics, methods of local spatial filtering. The following results were obtained. A method for synthesizing a universal mathematical model has been proposed for describing non-Gaussian signal characteristics of objects on multichannel images based on a multi-dimensional variant of Johnson SB distribution; this model was used for statistical pixel-by-pixel classification of the original satellite image. Algorithms for local post-classification processing in the neighborhood of the selected segments boundaries have been implemented. The analysis of the developed algorithms’ effectiveness based on estimates of classes’ correct recognition probability is performed. Conclusions. The scientific novelty of the results obtained is as follows: combined approaches to the pattern recognition procedures have been further developed – it has been shown that the use of methods of local object-oriented filtering of segmented images allows to reduce the number of point errors for element-wise classification of spatial objects.

article

English
Ukrainian

National Aerospace University «Kharkiv Aviation Institute», 2019.

классификация, аппроксимация, распределение джонсона, пространственная фильтрация, вероятность правильного распознавания, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95