## 2 Materials and Methods
### Data Preprocessing  
This study used the raster processing batch tool in ENVI 5.6 software to complete the preliminary processing steps, including radiometric calibration, atmospheric correction, and image cropping, followed by data annotation work. First, a multi-source data fusion strategy was adopted, combining high-resolution data from multiple satellites, including BJ-2, BJ-3, GF-7, SV-1, and JL-1, covering a time span from 2018 to 2022, with spatial resolutions ranging from 0.3 m to 0.8 m, which greatly reduced potential errors from single data sources. Secondly, regarding the standards for constructing and selecting image samples, we established strict screening criteria: cloud coverage in selected images was controlled below 10%, ensuring high image clarity without stripe noise and systematic distortion. Meanwhile, our study area comprehensively covers mangrove distribution areas in Beihai City, including typical ecosystems such as estuary regions, coastal zones, wetland protected areas, and wetland parks. Special attention was paid to collecting mangrove communities of different density levels, covering areas from sparse to dense growth while also considering various spatial distribution patterns, including continuous and scattered distributions. The implementation of these standards effectively ensured data reliability and accuracy. Finally, experts from geographical science and computer technology fields were brought in for collaborative annotation, combining GIS and remote sensing image interpretation techniques to enhance the professionalism and reliability of the annotations. Additionally, this study referenced historical data, particularly the Chinese Mangrove Long Time Series Satellite Remote Sensing Monitoring Dataset (1978-2018), ensuring the temporal continuity and spatial consistency of annotations. Figure 3 displays examples of the dataset's original remote sensing images and their corresponding annotation results, visually demonstrating the precision and professional level of the annotations. Furthermore, this study employed measures such as iterative optimization, strict quality control processes, and temporal consistency checks to ensure the reliability of the annotation results. This comprehensive approach not only overcomes the limitations of traditional manual marking in terms of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Satellite** & **Number of Images** & **Date** \\ \hline a, c & BJ-2 & 5 & 2 October 2018–22 November 2018 \\ b & BJ-3 & 1 & 23 December 2022 \\ b & GF-7 & 1 & 8 November 2021 \\ b & SV-1 & 1 & 12 October 2022 \\ a & JL-1 & 1 & 9 April 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information on remotely sensed data from the MSSDBG dataset. subjectivity, efficiency, and accuracy but also provides valuable experience and reference for large-scale mangrove monitoring and other remote sensing image annotation tasks. After annotation, this paper used a 512 \(\times\) 512-pixel sliding window (i.e., moving steps in horizontal and vertical directions) to crop large-scale remote sensing images, as shown in Figure 4, ultimately constructing a dataset containing 2568 remote sensing images. To ensure scientific training and objective evaluation of the model, the entire dataset was divided in a 7:2:1 ratio into mutually independent sets of 1798 training images, 514 validation images, and 256 test images. Firstly, this method satisfies the input requirements of deep learning models (which automatically learn features and patterns in data through multi-layer neural networks), as remote sensing images are typically too large to be directly input into standard memory deep convolutional neural network models. The 512 \(\times\) 512-pixel window size maintains a sufficient spatial resolution to capture target features while enabling efficient training and inference under existing hardware conditions.