# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 1 Introduction  
Land Cover Maps characterize essential information about the earth's surface and land usage. It helps to observe the subjects of some of the most critical topics of today's world, such as deforestation, urban and environmental monitoring and climate change. Namely, these maps give us tips about the future of our world. Generally, satellite optical imagery is used for land-cover mapping studies. Because several remote sensing platforms create representations with very high-resolution optical images, and detect all details about land, such as edge, shape and texture, and in very high-resolution images, resolution range is between submeter-scale and several meters [1; 2; 3; 4; 5]. Besides optical images, radar images are also used for mapping, and SAR is one of the most popular. SAR is an active sensor that transmits microwave signals to the earth and recovers signals that return or scatter from the earth's surface. SAR can be mounted on manned/unmanned aerial vehicles or satellites and is capable of high-resolution imaging, target detection in all weather conditions, including rainy or cloudy conditions, and provides images day and night. With these capabilities, SAR provides a wide range of uses due to its high-resolution images (some of which are exact and quickly processed) [6], target detection, images of the earth, evaluation of agricultural areas and forests and mass control of glaciers. With the increase in high-resolution SAR images [7], big SAR datasets have begun to form. Some frameworks help to label each pixel with different classifiers [8; 9; 10]. However, labeling SAR images still needs to be clarified and made easier. Thus, SAR imageshave too much noise due to its imaging procedure [11], complex backscattering [12] and geometrically-significant distortion [13]. When collecting data, soil moisture, vertical and horizontal form of the scatterers, roughness on the surface and geometry can obscure the SAR image because of the backscattering microwave radiation. In short, seasonal, temporal and environmental effects on the earth change the dataset and can cause shortages of generalization capabilities. These difficulties when obtaining SAR images, combined with the increased resolution made creating a new dataset almost impossible. Additionally, finding a labeled free dataset for scientific research is almost impossible. CORINE, a program started in 1985, aims to gather information for the European Union on priority environmental issues (e.g., air, water, soil, land cover, coastal erosion and biotropes). CORINE utilizes the land cover/use data produced by computer-aided visual interpretation methods over satellite images according to the Land Cover/Use Classification determined by the European Environment Agency (EEA) [14]. According to the EEA criteria and classification units (44 classes), changes in land cover/land use for monitoring the land via satellite images are detected with the help of remote sensing and geographic information systems. The main ideas of the CORINE projects are managing the environment, reducing the effects of climate change and helping to observe urban and environmental developments. SAR image processing is a challenging task [15]. One of the significant steps in the processing of SAR images is segmentation. The main aim of segmentation with SAR image is to split the image into parts according to separate attributes without any intersection and to mark each pixel in the image according to one of the classes [16]. With most accessible datasets, methods based on the many different convolutional neural network (CNN) [17; 18; 19] and the recurrent neural network (RNN) [20] have been used for the SAR image segmentation task, and successful results have been achieved. With the increase in access to big datasets with high resolution, pixel-wise segmentation has become impossible, since labeling SAR images is difficult. Superpixel generation models use superpixel-wise segmentation methods [21; 22] for big SAR datasets.