# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology  
### Dataset  
This study utilizes high-resolution remote sensing imagery from the Gaofen-2 (GF-2) satellite. Launched and operational since 19 August 2014, the GF-2 represents China's inaugural satellite dedicated to civilian use [19]. To optimize wetland extraction, data from April to October within the designated study area were emphasized. The period for data collection was carefully chosen, recognizing the profound impact of data quality on research results. For thorough coverage of Macau, GF-2 satellite imagery spanned three distinct scenes, with data specifically acquired on 26 August 2017 and 4 October 2018. The systematic selection and analysis of this data are crucial to ensuring the accuracy and credibility of the research outcomes.  
In this study, the preprocessing of GF-2 MSS images encompasses steps such as radiometric calibration, atmospheric corrections, and orthorectification, all executed using the ENVI 5.3 software. The PAN images underwent both radiometric calibration and orthorectification. Radiometric calibration adjusts raw imagery by converting digital numbers into reflectance values, enhancing overall image quality. Atmospheric correction removes distortions induced by the Earth's atmosphere, ensuring a truer representation of ground conditions. Orthorectification corrects for errors associated with terrain elevations, producing a geometrically accurate representation of the Earth's surface [20]. Subsequently, the PAN and MSS images undergo image fusion, a method that merges high-resolution panchromatic images with low-resolution multispectral images. This preserves the spatial details of the former and integrates the spectral information of the latter. The image is then cropped to the study region's specifications using ENVI software. Geometric corrections and image mosaicking are adeptly performed with the ERDAS 2016 software, aligning various images into a cohesive composite, facilitating an in-depth visual analysis [21].  
Sample generation is a critical step in this study. An 11 \(\times\) 11-pixel sample, chosen using the ArcGIS Pro 3.1 software, is intentionally selected to optimize compatibility with the training model. The specific dimension selection is designed to strengthen the correlation, enhancing the accuracy of predictions from the training model. Owing to the restricted availability of samples for artificial wetlands, this study implements downsampling techniques to attain sample equilibrium [22]. Figure 1 illustrates the technical approach employed in this research.  
Figure 1: Technical Route Overview.  
In this study, the band synthesis approach is centered around a 342-band combination scheme. This carefully devised scheme is designed to effectively distinguish various categories. Utilizing visual analytics, specific flags are interpreted to annotate diverse wetland types within the Macau region, facilitating the generation of pertinent samples. As an illustration, Figure 2a showcases the reservoir in Macau, exemplifying artificially constructed wetlands. Simultaneously, Figure 2b presents a sample from Macau's Cheoc-Van coastal mudflats, highlighting their distinct wetland ecosystem. Figure 2c further displays the coastal mangrove wetland adjacent to Lung Wan Lohas in Macau.