# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 1 Introduction  
As valuable natural resources, rivers occupy a more important position in cold and arid areas [1]. The ecological environment of river basins in cold and arid zones is fragile. The background information of river basins is very complex; however, remote sensing images can comprehensively obtain complex ground information, with the advantages of easy access, short cycles, and rich information [2, 3]. Therefore, it is possible to extract real-time, comprehensive, and accurate information about the river channel in cold and arid areas through remote sensing image monitoring, which is of great practical significance for the investigation, monitoring, and protection of water resources in cold and arid areas [4, 5]. The current traditional methods for river extraction from remote sensing images include morphology, thresholding, clustering, and classifiers [6]. [PERSON] et al. [7] proposedan iterative multiresolution decomposition using texture features combined with morphology to extract rivers from panchromatic satellite images. [PERSON] et al. [8] proposed a novel multi-stage river extraction method by combining grayscale thresholding and morphological features and correcting the results using morphological filtering. [PERSON] et al. [9] introduced a novel river extraction method that utilizes mathematical morphology training samples, Bayesian classifiers, and dynamically varying filters. This approach involves evaluating morphological erosion and dilation to generate a profile segmentation of the river. Additionally, the Bayesian algorithm is applied to enhance the segmentation results. [PERSON] et al. [10] used segmentation methods such as K-mean clustering and region growth to segment the color components of different regions in the satellite river image for river extraction. All the aforementioned traditional river segmentation methods necessitate the manual selection of feature parameters, limiting their applicability to simple scenes and presenting notable constraints for complex environmental segmentation [11]. In recent years, the application of convolutional neural networks in deep learning has become more and more widespread and has performed well in the task of river segmentation in remote sensing images, achieving better results [12; 13]. [PERSON] et al. [14] applied a convolutional neural network in the surface river extraction task and the results were significantly better than traditional water extraction methods. However, CNN, as an end-to-end, pixel-to-pixel network, reduces spatial information and increases the number of parameters when using a fully connected layer. In order to solve this problem, [PERSON] et al. [15] proposed a fully convolutional network (FCN) by replacing the fully connected layer with a convolutional layer. With the full convolutional networks proposed, many classical semantic segmentation algorithms, such as Unet, Linknet, PSPnet, Upernet, Deeplabv3+, Manet, Transunet, etc., have been proposed [16]. Among them, [PERSON] et al. [17] proposed a U-shaped structural model UNet based on a fully convolutional network in 2015. UNet is divided into two parts--the encoder and the decoder. The encoder makes constant convolution of the feature map for feature extraction, which preserves the texture detail information, and the decoder performs upsampling to preserve more semantic information. The encoder makes the layer-hopping connection with the decoder to enable the network to have more detailed semantic information. In 2017, Linknet [18] and PSPnet [19] were proposed, respectively. Linknet was proposed by [PERSON] et al. The structure of Linknet draws inspiration from Unet and uses Resnet18 as the encoder. It changes the concat operation of each channel to an add operation, which can, to some extent, reduce the computational and parameter load during the decoding process while ensuring accuracy and improving network speed. PSPnet was proposed by [PERSON] et al.