COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER
- 1Istanbul Technical University, Faculty of Civil Engineering, Department of Geomatics Engineering, 34469 Maslak, Istanbul, Turkey
- 2Istanbul Technical University, Earthquake Engineering and Disaster Management Institute, 34469 Maslak, Istanbul, Turkey
Keywords: Remote sensing, Object Based Classification, Machine Learning, Land Use / Cover, Support Vector Machine
Abstract. The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively.