# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 1 Introduction
Therefore, these methods' effects on complex flood areas still need to be improved. Some studies have proposed the use of conditional random field (CRF) processing at the pixel level [24] or association methods using the adjacent frequency of spatial object categories at the object level [25] to introduce spatial relationships. However, these methods are cumbersome and cannot acquire enough contextual information, which limits accuracy improvement. Deep learning (DL) methods have great performance in various segmentation tasks. Convolution neural networks (CNNs) have been widely used to identify the regions of interest in SAR images [26]. With its multi-layer network structure and strong learning ability, the DL method is an end-to-end classifier and does not need to design features manually. However, DL is a data-driven method requiring many samples in advance, which is time-consuming and costly [17; 24]. Another point is that the dataset is a sampling of the real environment, which means that the trained network only has a better effect on data with a similar distribution. It is particularly obvious in remote-sensing tasks that the performance of models declines sharply when used in a totally unfamiliar dataset. Moreover, the results of CNNs are easily confused at the boundary, especially in complex flood-inundated areas. Above all, CNNs are difficult to map in cases of sudden floods in different regions. In general, the TS method cannot accurately calculate the threshold and needs to be adjusted manually. ML methods treat each classified object as an isolated object and ignore their intrinsic relationship, which limits its accuracy. DL methods can achieve the highest accuracy, but they require large samples and computing resources, which are expensive, and their performance degrades when migrating across datasets. In recent years, graph neural networks (GNNs) have received extensive attention and have shown considerable potential in graphing data, one-shot learning, and optimizing CNN results. In the field of RS, GNNs have also been successfully applied to image classification [27; 28; 29; 30]. A significant reason for these successes is due to the aggregation ability of GNNs for global contextual information. The object-oriented analysis method can accurately capture the boundary and reduce the number of classified objects simultaneously, which is important for analyzing high-resolution images and beneficial for converting rule data into a graph structure. Thus, a GCN based on the object-oriented analysis method is proposed to overcome the abovementioned problems. The object-oriented method can capture the boundary of the object well, and the GCN establishes the spatial relationship between superpixels [31]. The proposed method is tested in a complex flood area and a lake compared with the traditional method. This paper is organized as follows: the method we propose is described in Section 2; Sections 2.1-2.3 describe the superpixel segmentation and feature extraction, sample selection, graph construction, and training, respectively; experimental results and analysis are demonstrated in Section 3; and the discussion and conclusion are provided in Sections 4 and 5, respectively.