доктор социологических наук;кандидат технических наук;
doctor of sociological sciences;candidate of technical sciences;
Описаны алгоритмы кластеризации цифрового изображения, которые используются в методике автоматизированного объектного тематического дешифрирования лесных дорог и других объектов по снимкам с беспилотных летательных аппаратов (БПЛА), полученным без дополнительного оборудования. Высокая достоверность дешифрирования является показателем эффективности алгоритма, разработанного с учетом методики обработки снимков БПЛА лесных дорог.
The procedures and algorithms automating a process of a multilevel subject decoding are of particular interest. In the paper there is described a development of algorithms for automatic object identification on the basis of clustering. In the investigation the algorithm for a cluster analysis of AKM (improved of k- means) which allows identifying first an object in the picture and then highlighting it graphically is used. This algorithm is formed on the basis of the k-means al-gorithm allowing the fulfillment of a rapid cluster analysis. The improvement of AKM algorithm consists in a possibility of the computation of an optimum cluster number at a specified maximum cluster number. The accuracy of the results of subject decoding is as-sessed. One of the methods for the assessment of relia-bility is a statistic assessment of picture decoding re-liability. For this it is necessary to create a matrix of errors at cluster definition and to calculate accuracy. It is possible to use a method of cross-tabulation for the presentation of pixels defined correctly in an obtained subject map of forest roads and a map formed on the basis of UAV pictures and data of ground investiga-tions. A general accuracy of object decoding as a re-sult of the work of AKM algorithm and a procedure of UAV picture processing of forest roads characterizes a degree of reliability as a high one. The options for the further improvement of a procedure and algorithms are offered.