## 2 Materials and Methods
### The Encoder  
We use ResNet-101 with atrous convolution as the backbone network for hierarchical feature extraction. ResNet is characterized by its ability to optimize residuals, making it highly suitable for constructing semantic segmentation networks. [PERSON] et al. [28] and [PERSON] et al. [2] utilized ResNet-101 as the backbone network to construct a framework that achieved significant results in water extraction tasks. Water bodies have different sizes and color texture features in remote sensing images and do not have specific topological structures. Therefore, for the extraction of water bodies, it is necessary to fully integrate high, medium, and low-level features to improve the segmentation performance. In this article, we initially downscale the input image resolution through a convolutional layer, followed by a max pooling layer. Subsequently, we extract semantic features by utilizing four residual blocks. Additionally, the Res-4 augments features through concatenated atrous convolutions with varying rates, thereby deepening the network and enriching the feature maps. For AFFM, we adopt the feature maps outputted by res-2 and res-3 as inputs, whereas the feature map derived from res-4 serves as the input for GCASPP. In the process of water feature extraction, the integration of multi-scale features is a pivotal approach to enhancing the segmentation performance. Consequently, for the purpose of extracting water bodies, it is indispensable in comprehensively integrating features  
Figure 2: The architecture of the global context attention feature fused network. across high, medium, and low levels. This paper proposes AFFM to fuse strong semantic high-level features and low-level detail features to improve feature expression, enhance the continuity of pixels in the image during subsequent upsampling, increase spatial and semantic information in the features, and thus improve object boundary segmentation. The specific structure of AFFM is illustrated in Figure 3. Firstly, the output of ResNet's third layer, res-3, is upsampled by 2 times. Then, the initial fusion feature map is obtained by concatenating res-2 and the upsampled res-3. Then, a \(1\times 1\) convolution operation is performed on the initial fusion feature map to reduce its dimensionality and parameter count. Subsequently, BatchNorm and Relu are introduced to prevent the occurrence of silent neurons and the vanishing gradient problem. Following this, spatial average pooling is performed, and inter-channel information interaction and learning are conducted through a \(1\times 1\) convolution operation. Additionally, an application of the sigmoid activation function is utilized to generate the attention weights. Finally, the initial fusion feature map is multiplied by the attention weights, thereby achieving refined fusion feature selection. A \(1\times 1\) convolution is then applied to reduce the number of fused feature channels to 256, matching the channel count of the high-level features processed by the GCASPP. The input feature map can undergo multi-scale feature extraction via the capabilities of the AFFM. This enhancement not only facilitates a detailed comprehension of the imagery's details but also augments the model's analytical and discriminatory abilities. In convolutional neural networks, due to the local receptive field characteristics of convolution operations, pixels with the same label may exhibit significant differences in the feature space, which can lead to intra-class inconsistency and thus reduce the accuracy of detection tasks. The spatial semantics obtained by sampling multiple pixels through convolution with different atrous rates in the ASPP module are a mixture of different target pixels, which limits the effectiveness of label prediction.