# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 1 Introduction  
Wetlands, characterized as diverse biological ecosystems, play a pivotal role in bridging terrestrial and aquatic environments, thereby sustaining life on Earth [1]. Their invaluable functions range from conserving biodiversity and supporting global hydrological cycles to mitigating climate change, highlighting their indispensable contribution to environmental balance. The rapid increase in global urbanization exerts significant stress on these ecosystems, emphasizing the urgency to adopt effective conservation measures [2]. The accurate identification, classification, and mapping of these areas are paramount, accentuating the importance of high-resolution satellite imagery for wetland segmentation. The wetlands in Macau, with their unique blend of coastal and urban attributes, provide an exemplary subject for understanding such complex terrains [3]. Previous research in wetland segmentation has extensively employed various data sources and technological advancements. Recent studies have leveraged various types of remote sensing images, including multispectral images (e.g., Landsat and Sentinel-2) [4], hyperspectral images (e.g., AVIRIS and Hyperion) [5], and synthetic aperture radar images (e.g., RADARSAT and ALOS PALSAR) [6]. Concurrently, the research on wetland remote sensing image segmentation has advanced significantly by employing a variety of techniques for classification and segmentation [7; 8]. These include not only traditional machine learning algorithms such as support vector machines (SVM) and random forests (RF) [9; 10], but also some deep learning models like U-Net and deep convolutional neural networks [11; 12]. Moreover, several studies have ventured into combining both texturaland spectral data to enhance the precision of wetland feature detection [13]. In the context of Macau's wetlands, the introduction of ResNet, a deep learning model, marked a significant advancement, primarily aimed at reducing misclassification of features, especially in the coastal regions of Coloane Island [14]. Despite the progress attributed to the ResNet model, certain gaps remain, impeding its optimal efficacy in wetland segmentation. For example, precisely characterizing coastal wetlands continues to pose challenges, with instances like Saiwan Lake erroneously classified as a constructed wetland. These limitations highlight the necessity for ongoing research dedicated to enhancing image segmentation methodologies for wetlands [15]. In response to these challenges, the current study employs advanced deep learning models to accomplish two primary objectives. Firstly, it improves the segmentation precision when compared to preceding research, and, secondly, it accurately categorizes Saiwan Lake as a constructed wetland [14]. Moreover, this research rectifies earlier limitations by effectively identifying areas up to 6 m above sea level on Coloane Island as coastal wetlands [15]. The RegNet model, a sophisticated segmentation method, shows significant performance improvement over the ResNet model, particularly in coastal wetland extraction and the classification accuracy of Saiwan Lake. A novel aspect of this research is the integration of the recently proposed Coordinate Attention mechanism with the RegNet model [16], resulting in the innovative CA-RegNet model. Although the model encountered misclassification issues around Coloane Island due to the inconsistent quality of the source image, the CA-RegNet model exhibited a marked improvement in segmentation performance metrics. Furthermore, this study introduces advanced wetland segmentation models such as Res2 Net, ConvNeXt, EfficientNet B7, Inception-RegNet, and SE-RegNet. These models are applied to wetland image segmentation, using the previously studied ResNet model for comparison. Notably, ConvNeXt, EfficientNet B7, Inception-RegNet, and SE-RegNet models exhibit superior performance in image segmentation, marking their inaugural application in wetland remote sensing image segmentation.