# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 1 Introduction
In order to reduce the loss of information between different regions, PSPNet proposes a hierarchical global prior structure (pyramid pooling module), which includes information from different sub-regions and corresponding scales, fuses features from different regions, and extracts global semantic information. In 2018, Upnet [20] and Deeplabv3+ [21] were successively proposed. Upernet was proposed by [PERSON] et al. Upernet created a multitasking dataset and designed the backbone and detection header of Upernet for the classification of different tasks. In Deeplabv3+, the introduction of null convolution allows the network to increase the sensory field without loss of information. Deeplabv3+ splices the feature maps obtained from interpolated upsampling through the spatial pyramid pooling module with the feature maps that have been downsized by the channel to give the network good detailed semantic information. In 2020, [PERSON] et al. [22] proposed a Unet segmentation network based on the Unet improved segmentation network, Unet++, and realized a finer segmentation scheme than the Unet network through nesting, as well as designing dense long and short jump connections to aggregate different semantic scales between the encoder and the decoder for the fine-grained detail aspects of the target object. In the following year of 2021, Manet and Transunet were also proposed. Manet, proposed by [PERSON] et al. [23], has a moderate receptive field to maintain degraded locality. It uses mutual affine convolutional layers to enhance feature expression without increasing receptive field, model size, and computational burden. The Transunet network structure proposed by [PERSON] et al. [24] also imitates Unet. It added a transformer mechanism in the encoder section and, ultimately, obtained a one-dimensional vector. The decoder section underwent three upsampling operations and ultimately restored this one-dimensional vector to the original image. The encoder and decoder drew inspiration from Unet and made three skip connections. The author believes that with the help of Unet, the transformer can achieve very powerful encoding functions by recovering local information. Some researchers have explored river extraction from remote sensing images based on classical semantic segmentation methods [6]. [PERSON] et al. [25] oriented to the segmentation of remotely sensed rivers in cold and arid regions, combining migration learning as well as introducing an attention mechanism, adding dense empty space pyramid pooling (DenseASPP) to the AR-LinkNet network to make the remote sensing river extraction finer and more coherent. [PERSON] et al. [26] addressed the problem that U-Net could not effectively extract the river contours from remote sensing images, carried out the improvement of the attention mechanism, and introduced the spatial pyramid pooling module, which optimized the network learning ability and retained the detailed information of the feature maps. [PERSON] et al. [27] combined the null convolution and the attention module to form a central region between the encoders where global feature dependencies can be accessed and performed river detail extraction from remotely sensed images. [PERSON] et al. [6] addressed the problem of low accuracy of river extraction by utilizing VGG16 [28] and ResNet50 [29] models for feature extraction. They introduced scale and semantic feature output layers to construct a two-branch fusion model and proposed a new method based on edge feature fusion with an accuracy of 97.7%. [PERSON] et al. [30] utilized deep and shallow features and attention modules for fine segmentation of watershed river edges, and then used conditional random fields (CRF) to further improve the segmentation accuracy of the edge region. The precision and IOU were 99.59% and 99.16%, respectively. Table 1 shows the above methods and their features. Although these methods have improved the extraction of river channel edge detail information from remote sensing images, there are still challenges.