## 1 Introduction  
Bedforms in river flows have an effect on bed roughness, flow conditions, and sediment transportation [1]. From an engineering perspective, they often present major navigation problems as they can reduce the local water depth. A large number of navigation channel regulations have been implemented and associated work has been conducted, which has changed the morphology and hydrodynamic behavior of rivers such as the Yangtze River. As bedforms are not static, it is also important to analyze them prior to laying pipelines and cables. In this respect, a multibeamechosounding system provides high-resolution bathymetric data that show the terrain of bedforms, and extracting the features of bedforms from bathymetric data has thus been the focus of intensive research in recent years. Many studies have focused on extracting the crest and trough of bedforms because height, wavelength, and asymmetry of bedforms provide an indication of the flow velocity and direction [2; 3]. In this respect, a bedform tracking tool was developed to automatically detect the locations of crests and troughs from the curves of profiles [4; 5; 6]. In addition, [PERSON] et al. [7] used a geostatistical filter to generate crest and trough lines as a set of points from the filtered bathymetric surface, and [PERSON] et al. [8] used geodesic morphology to extract the salient crest and trough lines of sand structures in three dimensions. However, for a flow with an increasing strength, many typical bedforms can be formed, such as ripples, dunes, anti-dunes, lower flat beds, and upper flat beds [9; 10; 11]. Flat beds are also identified as plane beds in some studies [12; 13; 14]. A flat bed is a widely spreading bedform in a river's subaqueous environment, and it may not contain many crest and trough features. The complicated development and spatial distribution of the bedform cannot be comprehensively identified using only crest and trough. In addition, scour pits can be contained in troughs with varying elevations with manual inspection, as indicated in many studies [15; 16; 17; 18]. Therefore, a greater detailed geometric element layer is required to describe the texture or roughness information of features such as flat and plane beds, and scour pits. Particularly, scour pits have an influence on the stability of underwater structures. Studies that previous classified terrestrial landform elements were used as a reference in this study, as the elements of flat, plane, and pit can be detected via variations in the terrain using these classification methods or techniques. [PERSON] [19] developed criteria for dividing the land's surface into six elements (plane, pit, peak, ridge, valley, and pass) based on four morphometric parameters (slope, cross-sectional curvature, maximum curvature, and minimum curvature). [PERSON] and [PERSON] considered the aforementioned four factors as the input vectors for the self-organization map (SOM) to classify terrain surface by training [20]. In the SOM technique, 10 elements are defined (including flat planar, gentle slope ridge, and gentle slope channel). [PERSON] et al. used geomorphons instead of geomorphometric variables to divide the surface into flat, pit, valley, ridge, peak, shoulder, fotslope, hollow, and spur elements based on the visual angle [21]. A fuzzy method [22] and an object-based method [23] have also been used to classify elements; however, they are relatively complicated to use. A few attempts have been made to apply [PERSON]'s criteria and geomorphons in the sea environment. For example, [PERSON] and [PERSON] [24] found that the use of geomorphons was superior to [PERSON]'s criteria for describing submarine sand waves, and [PERSON] et al. [25] modified geomorphons to detect seafloor hill, depression, ridge, valley, shoulder, and foot-slope. However, these methods have not been used in the subaqueous environment of rivers, and the approach and features with respect to using these methods to characterize river bedforms are currently unclear.