# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 4 Experiments
### Evaluation Metrics  
We performed a quantitative evaluation between ground truth and predicted masked images in a pixel-by-pixel comparison using precision (P), F1 score, recall (R), ED, ED\({}^{\prime}\), and intersection and integration ratio (IOU) as evaluation metrics. These metrics are commonly used for comprehensively evaluating binary river channel segmentation and assessing the accuracy of segmentation results. Among them, IOU is a metric that measures the accuracy of detecting the corresponding object in a given dataset. IOU can most intuitively and succinctly represent the effect of binary river channel segmentation in this paper. This metric is used to measure the correlation between the true and predicted values, and the higher the correlation, the higher the value [35]. To more intuitively and concisely depict the segmentation effectiveness in the experimental process of river channel extraction, this paper employs intersection and concurrency ratio values of the river category for evaluation testing during the experimental discussion comparing the mainstream and the CBAM module. Additionally, a series of experimental discussions are conducted using the IOU value as an index. For river segmentation extraction, the pixel distribution consists of the river and the background, with the pixels of the river set to 255 and the pixels of the background set to 0. There are four types of predictions: false positive (FP), false negative (FN), true positive (TP), and true negative (TN), respectively. FP indicates that the number of pixels of the river is incorrectly predicted (number of false positive samples). FN denotes the number of incorrectly predicted background pixels (number of false negative samples). TP denotes the number of correctly predicted river pixels (number of true positive samples). TN denotes the number of correctly predicted background pixels (number of true negative samples). IOU, Precision, Recall, and F1 scores range between 0 and 1. When these metrics are used to assess segmentation accuracy, the score corresponding to the classification result for each pixel reflects the final segmentation effect. The higher the score, the better the accuracy of river segmentation. The segmentation quality will be easy to recognize when the accuracy and recall of the target are both higher than another target. For example, when the accuracy and recall are both 0.8, the segmentation quality is significantly higher than when the accuracy and recall are both 0.6. However, it is difficult to determine the segmentation quality when two metrics of one target are not simultaneously higher than the metrics of the other target. For instance, when the accuracy and recall of the two targets are (0.8, 0.8) and (0.9, 0.7), respectively. To deal with this problem, the precision and recall metrics can be combined into a single one, and here, a combination of ED and ED\({}^{\prime}\) is used to capture the trade-off between the two metrics and give a clearer indication of segmentation quality [36]. Since the metric attribute values are close to 1, it is usually better to have a larger value for ED and a smaller value for ED\({}^{\prime}\). Figure 10: The loss descent curves of the model. The river precision (P) is defined as Equation (12):  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]  
Recall (R) denotes the proportion of the river pixel segmentation that was predicted correctly, as shown in Equation (13):  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{13}\]  
The F1-score is the reconciled mean of the accuracy and is calculated as shown in Equation (14) as follows.