employee
Moscow, Moscow, Russian Federation
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.
forest roads, unmanned aerial vehicles (drones) (UAV), subject decoding, clustering algorithms, error matrix
1. Jain, A. Data Clustering: A Review / A.Jain, M.Murty, P.Flynn // ACM Computing Surveys. - 1999. - Vol. 31. - № 3.
2. Information-Analytical Resource Devoted to Com-puter Training, Image Identification and Intelligent Analysis. - URL: www.machinelearning.ru/.
3. Chubukova, I.А. Lecture Course “DataMining” / I.А.Chubukova; Internet-University of Information technologies. - URL: www.intuit.ru/department/database/datamining/.
4. DataMining. Classification and Clustering. - Access Mode: http://bug.kpi.ua/stud/work/RGR/ DATAMINING/classifandcluster.html (address date: 01.07.2015).
5. Zaslavsky, А.А. Assessment of subjective classification coordination at specified classes / А.А.Zaslavsky, Т.А.Prigarina // Sociology: Metho-dology, Methods, Simulators. - 1994. - № 3-4.
6. Manylov, I.V. Comparison of algorithms for decoding aerial photos of agricultural lands / I.V.Manylov // Information-Control Systems. In-formation Processing and Management. - 2011. - № 3. - pp. 24-29.
7. GIS-Lab. Geographical Information Systems and Remote Sensing. Matrix of Errors and Computation of Accuracy Indices and of Subject Maps. - URL: http://gis-lab.info/qa/errormatrix.html (дата обращения: 10.07.2015).
8. Kozinov, А.P. Wavelet-transformation and oneclass classification for dam state monitoring / А.R.Kozinov, А.L.Peight, I.I.Mokhov, Yu.P.Ivanov // Information-Control Systems. Information Processing and Management. - 2014. - № 4. - pp. 24-32.
