Application of Machine Learning Classifier to Analyze Combined Passive and Active Remotely Sensed Data for Assessing Land Use/Cover Changes: A Case Study of Khon Kaen City
Keywords:
Support vector machine, Landsat imagery, Synthetic aperture radar imageryAbstract
Land-use planning and management in a metropolitan like Khon Kaen City necessitate an analysis of information related to change of land use/ land cover. At present, as raw data, active and passive remotely sensed images are provided free of charge. Therefore, using support vector machine classifier, the objective of this article is to compare the classification results obtained from the combined active and passive satellite imagery to the results from the use of only passive satellite imagery. The results illustrated that a map of land use/ land cover derived on the combined active and passive satellite imagery composing of Landsat and C-band SAR satellite imagery leads to accuracy of over 90%, which are higher than the use of only Landsat data.
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