Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm

Abstract

The object of research is the process of clustering images from space optical-electronic surveillance systems. The main hypothesis of the study assumed that experimental studies would make it possible to determine the number of clusters on images from space optical-electronic surveillance systems when using the k-means algorithm. The method of clustering images from space optical-electronic surveillance systems using the k-means algorithm, unlike the known ones, implies: – splitting the source image into Red-Green-Blue brightness channels; – determination of the Euclidean distance between pixels; – distribution of the entire set of image pixels into clusters; – recalculation of "centers" of each subset; – reassignment of new "centers" of each cluster; – minimization of the total intracluster variance. Experimental studies were conducted on the clustering of the original image using the k-means method at different values of k. It was established that with an increase in the value of k, the visual quality of clustering improves, and it is possible to visually determine a larger number of clusters in the images. To determine the number of clusters, the sum of clustering errors of type 1 and 2 at different values of k was evaluated. It was established that when the value of k increases, the sum of errors of the 1st and 2nd kind initially decreases exponentially. A further increase in the value of k does not lead to a significant decrease in errors of the 1st and 2nd kind. It was established that for a typical image from the space optical-electronic observation system, the value of k in the clustering method based on the k-means algorithm should be equal to 4. At the same time, the sum of errors of the 1st and 2nd kind is 31.3 %. Further research is directed to the development of clustering methods that reduce the sum of errors of the 1st and 2nd kind