# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 3 Dataset Creation  
Although most used datasets are detailed above, here we propose a novel pipeline that easily generates labels for SAR images. With our proposal, datasets varying in size can be created in accordance with the needs of the models. First of all, as shown in Table 1, C-band Sentinel-1 images with IW sensor mode and a ground range detected (GRD) product type, released freely by ESA and representing specific parts of Turkey, are obtained. These images are then preprocessed with the Sentinel Application Platform (SNAP) application. One of the most critical factors is preprocessing, which affects the success rate when training with Sentinel images. For Sentinel-1 grid data, a standard workflow is presented in [36]. The following steps have been used for preprocessing: [36]  
1. Thermal noise removal;
2. Application of orbit file;
3. Border noise removal;
4. Calibration;
5. Speckle filter;
6. Terrain correction.  
After this preprocessing procedure, images are exported in the high resolution RGB format to be used in the segmentation process. There are three different methods for obtaining RGB images: dual pol ratio, dual pol multiple and dual pol difference. When dual pole ratio is preferred, it uses the red VV channel, green VH channel, and blue VV/VH channel. For our study, three high resolution images were acquired with dual pol ratio. After that, these were cut apart to a pixel resolution of 256 \(\times\) 256, and 6722 images with 256 \(\times\) 256 pixel were acquired. Example images are shown in Figure 2.  
In order to use these images for supervised pixel semantic segmentation, the next step is acquiring labels for images. The CORINE dataset [14], freely available in Copernicus, was downloaded. In the original, this dataset consists of labels for 44 classes. In this study, with the help of QGIS program, we decreased the count of labels to five, as: forests, urban, peatland bogs and marshes, agricultural areas, and water.  
After the number of classes in the CORINE Land Cover dataset was reduced to five, three images with the same resolution and coordinates were obtained from the SNAP via the QGIS. As a result, labeled RGB images with the same resolution and coordinates were made ready for semantic segmentation.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & Istanbul, Turkey \\ \hline Size & 12,228 \(\times\) 12,228 px. \\ Geo Coordinates West, East & 28.443, 29.546 \\ Geo Coordinates South, North & 39.96, 41.063 \\ Reference Information & 20210123 036260 0440 DA 330D \\ \hline \hline  & Izmir, Turkey \\ \hline Size & 12,228 \(\times\) 12,228 px. \\ Geo Coordinates West, East & 26.604, 27.703 \\ Geo Coordinates South, North & 38.654, 39.752 \\ Reference Information & 20210128 025357 03051D 2D81 \\ \hline \hline  & Adana, Turkey \\ \hline Size & 12,228 \(\times\) 12,228 px. \\ Geo Coordinates West, East & 34.906, 36.004 \\ Geo Coordinates South, North & 36.643, 37.742 \\ Reference Information & 20210120 025240 03015D DC9A \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the newly-created dataset.  
Figure 2: Examples of 256 \(\times\) 256 RGB images.