INCREASING THE SPATIAL ACCURACY OF THE LAND USE MAP USING FUSION OF OPTICAL AND RADAR IMAGES OF SENTINEL AND GOOGLE EARTH ENGINE

Abstract

Nowadays, exact and real-time land use and land cover (LULC) maps are imperative to supply precise data for energetic observing, arranging and arrive administration. With the appearance of cloud computing frameworks, time arrangement includes extraction methods, and machine learning classifiers, unused openings emerge in more precise and larger-scale LULC mapping. In this research, the aim is to obtain a land use map with a spatial resolution of 10 meters by combining Sentinel-1 radar images and Sentinel-2 optical images. The processing was done in Google Earth Engine cloud computing system. Also, in this research, NDVI map and NDBI map were used to increase classification accuracy. Classification was done using the decision tree method and in eleven classes. To evaluate the classification accuracy and the final map, randomly collected points were used in the study area. The results of the comparison of the ground collection points and the final classified map showed that the classification accuracy in this study was around 94%. The results obtained from such as research can be optimally used in urban planning and crisis management.