## 3 Methodology  
### Proposed Deep Learning Architecture  
This study introduces a novel Dual-Stream feature extraction network, termed DSST-UNet, which synergizes principles from CNN and Transformer models, specifically Swin-Unet, to enhance water bodies' extraction using fused multimodal data, including optical and SAR imagery. Inputs to the network are distinct patches from both optical and SAR images. The comprehensive DSST-UNet architecture is depicted in Figure 1: 1(a) outlines the entire network, 1(b) details CNN branch (DCB-ConvNet), and part 1(c) elucidates the elements within 1(a). DSST-UNet encoder, dedicated to feature extraction and fusion, cooperates through three main steps:  
(1) Pseudo-Siamese feature extraction: Illustrated in Figure 2, this phase involves the parallel processing of optical and SAR image patches through two distinct sub-networks within Pseudo-Siamese feature extractor, each functioning independently without shared weights. Each sub-network yields four hierarchical feature maps, simultaneously decreasing in spatial dimensions and increasing in channel depth. Specifically, the optical images produce feature maps {O1, O2, O3, O4}, and SAR images yield {S1, S2, S3, S4}, with corresponding spatial resolutions of {128, 64, 32, 16} and channel counts of {96, 192, 384, 768}. Subsequently, a fusion process combines corresponding feature maps from the optical and SAR pathways, resulting in an integrated feature set denoted as {C1, C2, C3, C4}. (2) Dual-Stream feature encoding: As outlined in Figures 3 and 4, the encoder's initial three stages involve a parallel configuration of a Transformer branch and a CNN branch (DCB-ConvNet). The Transformer branch employs a self-attention mechanism to assimilate the global context across varying image locations, thereby securing comprehensive semantic insights. Conversely, the CNN branch (DCB-ConvNet) harnesses an enhanced D-CBAM to meticulously discern local attributes such as texture, contours, and shapes, augmenting the system's proficiency in local detail recognition of diverse water body forms. These branches operate concurrently, generating a feature map that amalgamates both global and local image information. During this phase, the merged features from each optical image stage are labeled {O1, O2, O3}, and those from each SAR image stage are {S1, S2, S3}. Subsequent to this, a fusion procedure combines the stage-specific optical image feature maps with those of SAR image to create multimodal features. This fusion involves channel-dimension splicing of O1 with S1 to form a novel feature tensor, followed by a dimension-reduction technique (using 1 \(\times\) 1 convolution) that rescales the spliced tensor down to C1. This method of splicing and dimension reduction is consistently applied to O2 with S2 and O3 with S3, yielding the compounded features C2 and C3. This dual-stream encoding strategy effectively merges the multimodal data from the optical and SAR images, resulting in integrated feature maps from the first three stages {C1, C2, C3}. Figure 1: Overview of the network structure. Figure 1: (**a**) outlines the entire network, (**b**) details CNN branch (DCB-ConvNet), and part (**c**) elucidates the elements within (a). Figure 2 corresponds to (1); Figure 3 corresponds to (2); Figure 4 corresponds to (3); Figure 5 corresponds to the process of fusion of multi-source data features at each stage of the process in (2) and (3); Figure 6 corresponds to D-CBAM; Figure 8 corresponds to FPA. Figure 2: Structure of Pseudo-Siamese feature extraction. (3) Advanced feature encoding: As depicted in Figure 5, the final segment of the encoder phase involves the intricate integration of the Transformer branch with a FPA module. Within this process, the Transformer branch operates independently on the designated optical and SAR image segments, dedicated to nuanced feature extraction. Following this, a FPA module [33] is applied to the preliminary extraction outcomes, producing refined outputs O4 and S4.