## 2 Materials and Methods
### Data Preprocessing
Secondly, this cropping method helps optimize computational resources by processing large-scale remote sensing images in batches, reducing resource requirements for single computations and improving computational efficiency and parallel processing capabilities. Furthermore, the 512 \(\times\) 512 window size preserves local spatial details while covering sufficient ground feature information, helping models better learn and recognize complex ground features such as mangrove distribution and morphology. By cropping different image blocks and combining sliding window overlap settings, more diverse training samples can be generated. Smaller image blocks are also more conducive to data augmentation operations, helping improve model generalization ability and reduce overfitting risk. Setting overlap regions ensures the preservation of boundary details when recombining image blocks, which is particularly important for maintaining spatial continuity and improving overall classification or detection accuracy. Finally, processing smaller image blocks helps better manage computer memory and avoid memory overflow issues. In conclusion, this cropping method not only meets the technical requirements of deep learning models but  
Figure 3: Examples of Original Remote Sensing Images and Their Corresponding Annotation Results. also ensures image information integrity while improving processing efficiency and model performance, holding significant importance for large-scale remote sensing image analysis and mangrove monitoring. After completing the cropping process, this paper performed data augmentation operations, including random image resizing, random cropping and flipping of images and segmentation maps, and photometric distortions. Photometric distortions (such as adjusting brightness, contrast, and saturation) simulated the effects of different lighting conditions and sensor variations on remote sensing images, thereby improving network adaptability across various environments. Random cropping and flipping operations helped the network learn position- and orientation-invariant feature representations, which is crucial for identifying targets (such as mangroves) in different directions and positions within remote sensing images. These techniques significantly increased the diversity of the training data, helping the network learn more robust and generalized feature representations while reducing the risk of overfitting. When training data were limited, data augmentation effectively expanded the training set, improving the network's few-shot learning capability. Additionally, these operations enhanced model robustness to input perturbations and helped balance imbalanced datasets. In summary, through these data augmentation strategies, we were able to fully utilize limited training data to build more reliable and efficient deep learning models, which is significant for remote sensing image analysis and mangrove monitoring. In conclusion, this study successfully overcame many limitations of traditional manual annotation in mangrove monitoring through multi-source data fusion, expert collaborative annotation, and effective data cropping and augmentation strategies. The proposed comprehensive data preprocessing method not only improved annotation accuracy and efficiency but also significantly enhanced model generalization ability and adaptability, providing a solid technical foundation for large-scale remote sensing image analysis. This  
Figure 4: Schematic diagram of the original and cropped images. method not only demonstrated significant advantages in mangrove monitoring but also provided a valuable reference for other types of remote sensing image annotation tasks, offering broad application prospects and promotional value.