Detection of Rice Fields in Sleman District using SVM (Support Vector Machine) Method

Abstract

Remote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To obtain information on the area of the rice field from the interpretation of Landsat-8 OLI imagery, a special method is needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method.This research is useful to implement the SVM method and support research in the geospatial field. This study aims to determine the area of Sleman district rice field with SVM and to determine the accuracy of the SVM method. The Support Vector Machine (SVM) method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The SVM method is used to classify segment polygons in which similar pixels.