# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 2 Related Works
### SAR Processing Models  
Studies have shown that many different datasets have been used in many areas, such as road segmentation, land cover, wetland classification, etc.  
For road segmentation, DeepLabV3+, FCN-8s and Deep Residual U-Net deep neural models were compared in [17]. X-band TerraSAR-X images were trained and the resulting IoU value for FCN-8s was 45.46%, the precision value was 71.69% and the recall value was 75.17%. Meanwhile, Deep Residual Unet IoU's value was 40.18%, while DeepLabV3+ IoU value was 40.18%.  
Some efforts have also been made on the land cover segment. There were comparisons of roads, built-up areas, and water and vegetation via POLSAR image segmentation with MIoU values and FcN and Unet models in [18]. With the present study, we aim to increase the success rate by using small datasets. While the FCN mean IoU value was 0.50, the U-Net mean IoU value was 0.44. In ref. [31], Sentinel-1 SAR images were used with state-of-the-art segmentation models such as U-Net, DeepLabV3+, FC-DenseNet, BiSeNet, PSPNet, SegNet and FRRN-B; also, the results were compared based on their accuracy. The best overall accuracy through these experiments was achieved by the FC-DenseNet (Fully Convolutional DenseNets) model, with 92.78%.  
In ref. [19], a novel architecture was proposed with polarimetric SAR (PolSAR) imagery, and it classified the wetland complexes. This work compared results from RF, FCN-16s, FCN-8s, FCN-32s and Segnet models. The F-1 score of the proposed FCN model was 0.84, and the F1-score of the RF model was 0.74. F1-scores achieved by FCN-32s, FCN-16s, FCN-8s and Segnet models were as follows: 0.69, 0.75, 0.81, 0.76, respectively. The proposed FCN model had an increased F1 score when compared with existing models.  
In ref. [32], a novel matrix factorization active-contour model based on fused features was proposed for water/land segmentation in SAR images. Wavelet textual features, Gaussian (DoG) filter features, and Gabor filter features were compounded, and feature matrixes were composed. Then, with the SAR image's edge and region data, the energy procedure was created.  
Generally, unsupervised image segmentation models are preferred in SAR segmentation because they are easy and useful. In ref. [33], a novel model with region smoothing and label correction (RSLC) was explained in detail. To resemble the spatial information in the polynomial result of the image, smoothing results were used. This establishes that the process of segmentation may be accomplished rapidly and effectively. One of the other effective and thriving segmentation projects is a convolutional deep neural network (CDNN) [34], which has been proposed to classify crop areas within SAR satellite images, along with the cultivation status of the crop. Using a high pass linear spatial (HLS) filter, the segmented dataset is preprocessed in training. After that, a modified region growing (MRG) algorithm is used for classification in the testing phase, and with Euclidean distance (ED), the cultivation status of each crop can be specified. Additionally, a new method was proposed in [35], based on brief presentation and a hierarchical fuzzy C-means (FCM) advance. Firstly, the image is divided into pixel groups with their features, and the major pixels which consist of pixel groups are then used for constructing the thumbnail. After that, FCM is utilized for segmentation.  
Our work differs from those mentioned above in that it explains the new dataset creation process from beginning to end. After that, popular semantic segmentation methods are used and evaluated with mIoU, MA, and PA metrics with our newly-created dataset. In this way, with this paper, a detailed study has been proposed, proceeding from the creation of a SAR dataset to explaining how it can be used with deep learning methods.