## 1 Introduction
### Current Research Status  
In recent years, significant progress has been made in mangrove wetland monitoring with the rapid development of remote sensing and deep learning technologies [16; 17]. Early mangrove remote sensing monitoring primarily relied on medium to low-resolution satellite data. Researchers mainly used satellites like Landsat and Sentinel for mangrove distribution monitoring but were limited by low image resolution and complex data preprocessing steps. To improve wetland classification accuracy, research shifted toward using high-resolution drone data and satellite imagery (such as GF, BJ, and SV series). Traditional monitoring methods mainly employed shallow machine learning algorithms like Random Forest (RF) [18], Support Vector Machine (SVM) [19], and object-oriented approaches for mangrove extraction and spatiotemporal analysis based on pixel classification. These methods required manual feature design and intermediate semantic features, lacking flexibility and adaptability when dealing with multi-scale targets and inconsistent category distributions like mangrove wetlands. With the rapid advancement of deep learning technology [20; 21], new research directions for semantic segmentation emerged, providing novel solutions for mangrove monitoring. The introduction of Fully Convolutional Networks (FCNs) [22] pioneered end-to-end, pixel-level classification, using deconvolution to upsample the final feature map to input image size. However, FCNs showed inadequate feature extraction and loss of detailed information during upsampling when processing mangrove images, resulting in  
Figure 1: Several challenges in mangrove remote sensing image semantic segmentation. poor performance in mangrove wetland semantic segmentation. Subsequently, numerous semantic segmentation networks were proposed. PSPNet [23] aggregated a context from different regions through Pyramid Pooling Modules and pyramid scene parsing networks, enabling global context understanding. However, its fixed-scale pooling operations struggled to adapt to the varied morphological features of mangrove wetlands. DANet [24] introduced spatial and channel attention mechanisms to balance local features and global dependencies. PSANet [25] explored position-related attention mechanisms by adaptively predicting global attention maps for each position in feature maps through convolutional layers, improving the feature recognition of scattered wetlands but lacking accuracy in mangrove wetland edge regions. DeepLabv3 [26] used Atrous Spatial Pyramid Pooling to collect different-scale receptive fields and effectively expanded filters without increasing parameters and computational complexity but still faced insufficient receptive field issues when processing high-resolution remote sensing images. DeepLabv3+ [27] introduced dilation rates for dilated convolution and improved Atrous Spatial Pyramid Pooling, achieving better segmentation results but remaining inadequate in handling complex mangrove boundary regions. K-Net [28] achieved multi-task unification by assigning different kernels to each task, but its dynamic kernel generation process was computationally expensive. PIDNet [29] implemented semantic segmentation and boundary detection but showed suboptimal detection accuracy for targets with special growth patterns like mangroves. While UperNet [30] could parse visual concepts like cross-scene categories, objects, and textures while performing scene recognition, object detection, and semantic segmentation tasks, its feature pyramid structure showed insufficient information transmission when processing multi-scale mangrove features. In summary, directly applying these methods to high-resolution mangrove remote sensing image semantic segmentation still faces many challenges. Firstly, the existing models are mostly designed for natural scene images and show inadequate feature extraction when processing remote sensing images with special spectral and texture features, particularly in areas where mangroves mix with other vegetation.