DBPapers
DOI: 10.5593/SGEM2014/B23/S11.095

METHOD OF ERROR ASSESSMENT IN IMAGE CLASSIFICATION

D. Bartonek, J. Bures,I. Opatrilova, A. Vitula
Wednesday 1 October 2014 by Libadmin2014

References: 14th International Multidisciplinary Scientific GeoConference SGEM 2014, www.sgem.org, SGEM2014 Conference Proceedings, ISBN 978-619-7105-12-4 / ISSN 1314-2704, June 19-25, 2014, Book 2, Vol. 3, 745-752 pp

ABSTRACT
The article describes a method for the determination of error rate in automated image classification and the improvement of accuracy of results by the filtering using the selected vector datasets of ZABAGED (Fundamental Base of Geographic Data in the Czech Republic) in geographic information system (GIS). The principle of method is based on metric spaces and uses the interpretation of three types of metrics: euclidian, time and thematic. In our case, the evaluation of error rate of the classification results is based on the thematic distance. Special method of assessment was proposed for this type of distance and its essence is a classification tree encoded into chain codes. The distance between values of thematic data is determined by comparing their chain codes. The method is semi-automatic with controlled degree of objectivity of achieved results. The proposed method was tested in the project of data analysis of storage of gas facilities under certain types of terrain surface in the Czech Republic (CR). This analysis was done in order to determine reproductive values of gas facilities (pipelines) and the valuation of costs which would be necessary to spend for building new networks. The authors solved this project for the GasNet, Ltd. Company which is a part of a RWE group in the CR. Input data were raster datasets of orthophoto with the resolution of 25 cm/1 pixel and vector layers of the route of communications of the ZABAGED CR. Due to the territorial coverage of the CR with the area of 64,350 km2, these were massive tasks with data volume of 500 GB. The whole data analysis was carried out in ArcGIS 10.0 environment with using purpose-built applications in Python language with support for ESRI libraries. The used technology of data analysis demonstrated the low error rate in the range of 2% - 3% on the whole modeled area.

Keywords: Error assessment, image classification, GIS

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