# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 4 Results Show
This score becomes particularly crucial when there is anecessity to maintain a balance, ensuring that neither precision nor recall is compromised at the expense of the other, thereby offering a synchronized evaluation of model efficiency, as delineated in Formula (7):  
\[\text{F1-Score}=(2\times\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall}) \tag{7}\]  
These metrics provide a nuanced comprehension of the model's efficacy, elucidating its strengths and areas for improvement in landscape feature delineation. Figure 11 subsequently showcases the performance metrics--OA, Kappa coefficient, mAcc and mIoU--of seven distinct models, including the benchmark, ResNet model. A thorough analysis of these metrics across the examined models reveals key insights into their competency in wetland landscape feature classification. As indicated by the data, four models--CA-RegNet, SE-RegNet, Inception-RegNet, and RegNet--outperform the benchmark ResNet model, demonstrating superior OA and Kappa Coefficient scores. Specifically, the OAs recorded for CA-RegNet, SE-RegNet, Inception-RegNet, RegNet, and ResNet are 0.9749, 0.9632, 0.9615, 0.9706, and 0.9479, respectively. Similarly, the Kappa coefficients stand at 0.9624, 0.9449, 0.9422, 0.9560, and 0.9219. The mAccs are 0.9652, 0.9620, 0.9625, 0.9637, and 0.9466, in the same order, while the mIoUs are 0.9517, 0.9524, 0.9591, 0.9615, and 0.9466. These improved metrics highlight the enhanced capability of these models to identify and classify the complex components of wetland landscapes, marking a significant advancement over the ResNet model. Most prominently, the CA-RegNet model proves to be the superior system, with three out of its four accuracy metrics leading the ranks, showing a 2.7% increase in OA, a 4.0% rise in Kappa coefficient, a 1.9% enhancement in mAcc, and a 0.5% growth in mIoU compared to the benchmark. Such marked enhancement underscores the CA-RegNet model's prowess in precisely and consistently identifying the diverse features inherent within wetland landscapes. The OA, the Kappa coefficient, the mAcc and the mIoU serve as comprehensive metrics, offering insights into a model's predictive accuracy and the congruence between predicted classifications and actual observations [45]. The elevated values achieved by the CA-RegNet model in these metrics underscore its enhanced predictive capabilities and alignment in categorization, emphasizing its potential for advanced wetland feature  
Figure 11: Model Performance Metrics. classification. Nonetheless, it is pivotal to acknowledge that while these metrics furnish valuable comparative insights, they present a holistic perspective [46]. As such, they ought to be complemented with metrics like user's and producer's accuracy, F1-Score to facilitate a meticulous assessment of model performance across discrete landscape features, as detailed in Table 1. Upon a holistic analysis, the CA-RegNet model registers the pinnacle in both producer and user accuracy across categories. The normalized matrix for each model is depicted in Figure 12, and the confusion matrix of each model is shown in Figure 13.  
Figure 14 presents segmentation results from various models applied to the original GF-2 remote sensing image of Macau. Figure 14a displays the original image, while Figure 14b through Figure 14i delineate the segmentation outcomes of ResNet, Res2 Net, RegNet, EfficientNet B7, ConvNeXt, Inception-RegNet, SE-RegNet, and CA-RegNet models in the corresponding order. Within these figures, dark blue regions represent coastal wetlands, and light blue areas denote constructed wetlands. A visual analysis commences with the ResNet model, serving as the reference, depicted in Figure 14b. This model adeptly mitigates disturbances from uneven raw image quality and cloud cover, particularly in the Coloane Island Sea area, accurately classifying regions beyond the 6 m sea level [47].