# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 1 Introduction  
Surface water is important in maintaining the biosphere and the basis of life [1] and severely impacts ecosystems and human activities. However, floods are one of the most destructive natural disasters in the world, posing tremendous damage and threat to people's safety and social security [2], and about 10 to 20 million persons are affected by floods every year [3]. With an increasingly abnormal global climate and extreme precipitation, flood events are occurring more frequently and causing more serious losses than before [3, 4]. Therefore, the rapid acquisition of surface water bodies benefits resource investigation, emergency relief, post-disaster management, etc., which is of great research significance [4, 5]. Remote sensing (RS) techniques have the potential for highly detailed, high-temporal-resolution, high-efficiency, and large-scale surface water mapping. Compared to optical RS imagery, synthetic aperture radar (SAR) has advantages and is usually the prime candidate for the task [6, 7]. First, SAR works in the active mode and is independent of sun illumination and weather conditions (capable of penetrating clouds and rain). Second, it can easily differentiate the roughness of land and water bodies. Through double or multiple scattering on a rough land cover surface, the sensor can acquire a high amount of ground signals. Specular backscattering tends to occur when the radar signal reflects off avery smooth water surface, sending most of the signals away from the sensor and creating dark areas in SAR images. In recent years, SAR technology has advanced by leaps and bounds, and various satellites such as sentinel-1 and GF-3 have been launched. They can provide many images with low cost, large coverage, and over a short time period, which greatly reduces the obstacles of using SAR data. Polarimetric SAR (PolSAR) refers to the data with additional polarimetric information and is the orientation of the plane in which the radar wave oscillates. Different polarization modes carry information about the different scattering mechanisms and structures of the imaged surface [8; 9]. Thus, multi-polarization data always perform better in various segmentation and classification tasks than single-polarization, and likewise, it is preferred in the extraction of surface water bodies [2; 10; 11]. Currently, many extraction methods of surface water bodies can be used. Since the scattering intensity of water bodies in PolSAR images is usually significantly lower than that of land covers [12], threshold segmentation (TS) can be directly applied to the intensity or composite images. Some commonly used algorithms include the Ostu algorithm, adaptive global method, entropy threshold method, minimum error threshold method [13; 14; 15], and active contour model (ACM) [16]. Due to the advantage of requiring no additional information, low computational complexity, and ease of use, TS is still the most frequently used method in disaster emergency scenarios [17]. However, TS methods have some natural defects, including: (1) the specific threshold value cannot be calculated quickly and often needs to be adjusted manually for better results, which means the accuracy is affected by subjective factors of the selector. (2) The segmentation result produces many noise patches, which requires a series of post-processing activities [18; 19]. The supervised machine learning (ML) method adopts artificial design features, such as texture, terrain, and color at the pixel level, and classifiers such as support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) to extract surface water [20; 21]. In addition, the object-oriented analysis method is also commonly used by extracting features from the object of the superpixel [22; 23]. Superpixel techniques segment images into regions by considering similarity measures defined using perceptual features. These methods often regard classification units as independent individuals and ignore their spatial relationships.