# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 4 Results Show  
In this research, the effectiveness of each model, inclusive of the benchmark ResNet, is assessed using the overall accuracy (OA) (aAcc), the Kappa coefficient, the mean accuracy (mAcc) and the mean intersection over union (mIoU). These metrics provide an encompassing understanding of a model's capability in classifying distinct features within wetland landscapes. Specifically, the overall accuracy (OA) denotes the proportion of correct predictions rendered by the model [43]. This metric quantifies the total accurate predictions relative to the complete dataset, offering an unambiguous assessment of the model's proficiency in wetland landscape classification. It is determined by the summation of correct predictions (true positives and true negatives) over the total observations, as expressed in Formula (1). \[\text{OA}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{FN}) \tag{1}\]  
In the provided context, TP denotes true positive, TN signifies true negative, FP stands for false positive, and FN is the false negative. The Kappa coefficient (k) quantifies the concurrence between the model's predictions and actual observations, accounting for the random agreement by chance. Fundamentally, this parameter illustrates the extent to which the model's predictions surpass those made at random, providing a stringent analysis of the actual performance of the model, as delineated in Formula (2):  
\[\text{Kappa Coefficient}=(\text{Po}-\text{Pe})/(1-\text{Pe}) \tag{2}\]  
Mean accuracy (mAcc) stands as a metric computing the average classification accuracy across distinct classes. This measure aims to furnish a comprehensive perception of the model's performance breadth over diverse categories, pertinent particularly in contexts of potential dataset imbalance. The calculation for mAcc is as follows:  
\[\text{mAcc}=1/\text{N}\ \times\sum_{i=1}^{\text{N}}\text{TP}_{i}/(\text{TP}_{i }+\text{ FN}_{i}) \tag{3}\]  
Mean intersection over union (mIoU), a standard metric in segmentation endeavors, quantifies the overlap degree between the predicted and true classifications. This parameter reflects the model's proficiency in identifying class boundaries and intricacies, proving essential in tasks that require stringent precision:  
\[\text{mIoU}=1/\text{N}\ \times\sum_{i=1}^{\text{N}}\text{TP}_{i}/(\text{TP}_{i }+\text{ FP}_{i}+\text{ TN}_{i}) \tag{4}\]  
Here, Po represents the relative observed agreement ratio, while Pe is the hypothetical probability of random agreement. For finer evaluation granularity, user's accuracy (UA) (Recall) and producer's accuracy (PA) (Precision) and F1-Score are utilized to measure the classification precision of each category within the models. UA conveys the likelihood that a pixel, as classified on the map/image, truly corresponds to that class on the ground [44]. The metric underscores the model's adeptness at accurately pinpointing a specific class, establishing itself as a crucial measure when precision in individual class identification is paramount. It is determined by the ratio of correct class predictions (TP) to the total predictions for that class (sum of TP and FP):  
\[\text{UA}=\text{TP}/(\text{TP}+\text{FP}) \tag{5}\]  
Conversely, PA, also known as precision accuracy, indicates the probability that a pixel representing a specific class on the ground is appropriately classified. This metric becomes pivotal when the cost of false positives is high, providing insights into the reliability of a model's positive predictions for each class. It is the ratio of correct predictions for a class (TP) to the total instances of that class (sum of TP and FN):  
\[\text{PA}=\text{TP}/(\text{TP}+\text{FN}) \tag{6}\]  
The F-1 score amalgamates precision and recall, forming a consolidated metric that underscores their equilibrium.