# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 1 Introduction
For example, there are fewer publicly available professional remote sensing river segmentation datasets in cold and dry areas. Texture and edge features are essential research directions for river channel segmentation, and the traditional river channel extraction methods are unable to finely extract the edge information of the river channel, as well as some classical deep learning algorithms. Additionally, the fine river branches around the river and the small non-water area blocks in the middle of the river cannot be detected precisely and completely. Aiming at the problems of many river branches and frequent river transformations in cold and arid areas, as well as the inability to accurately extract the detailed information of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods Classification** & **Author and Date** & **Methods Feature** \\ \hline \multirow{4}{*}{Traditional methods} & [PERSON] et al., 2012 [7] & Morphology \\  & [PERSON] et al., 2015 [8] & Morphology, threshold \\  & [PERSON] et al., 2016 [9] & Morphology, classifier \\  & [PERSON] et al., 2017 [10] & K-means clustering \\ \hline \multirow{8}{*}{Deep learning methods} & [PERSON] et al., 2021 [30] & GAU + CRF + Unet++ \\  & [PERSON] et al., 2022 [26] & Enhanced Attention + \\ \cline{1-1}  & [PERSON] et al., 2022 [27] & DenseASPP + Unet \\ \cline{1-1}  & [PERSON] et al., 2022 [27] & Dilated convolution + CBAM \\ \cline{1-1}  & & + D-LinkNet \\ \cline{1-1}  & [PERSON] et al., 2022 [25] & ResNet101 + DenseASPP + \\ \cline{1-1}  & & AR-LinkNet \\ \cline{1-1}  & & VGG16 and ResNet50 + The \\ \cline{1-1}  & [PERSON] et al., 2023 [6] & Semantic and scale feature \\ \cline{1-1}  & & output layer \\ \hline \hline \end{tabular}
\end{table} 
Table 1: River segmentation methods. river edges, a river channel extraction method for remote sensing images in cold and arid areas is proposed. The main contributions of this paper are as follows:  
1. A remote sensing dataset for river segmentation in cold and arid areas was produced based on Landsat8 OLI images, containing 1900 512 \(\times\) 512 564-channel false-color remote sensing images, targeting the 1321 km Tarm River mainstem in the Xinjiang region of China. 2. We use the first 13 layers of vgg16 as the encoder to extract river features, and the CBAM [31] attention module is introduced at the end of the encoder to make the shallow feature extraction of the texture and edges of the river more adequate. 3. We propose a residual attention feature fusion module (RAFF). The RAFF module is added at each short jump connection in the network to optimize the combination between edge texture information and river semantic information by Unet++ long and short jump connections so that the network finally obtains more accurate semantic information of river details, which helps to realize the fine segmentation of river edges. 4. In this paper, a weighted loss function consisting of CE loss, dice loss, focal loss, and Jaccard loss is used to reduce the training error of river remote sensing image segmentation and to realize the pixel-level optimization of medium-resolution river remote sensing images.