## 3 Materials and Methods
### UniU-Net  
To enhance the automation of area extraction and reduce the reliance on domain expertise, this study builds upon the classic U-Net framework [32] to propose a newly redesigned deep learning model named UniU-Net, as shown in Figure 2. Given the multispectral nature of the input data, a single convolutional encoder may not fully capture the distinguishing features of area palms. Although increasing the depth of a single encoder could improve the classification accuracy, doing so also raises the risk of overfitting and introduces structural redundancy.  
Grounded in the concept of structural reparameterization, a parallel design can improve the comprehensiveness of feature learning and mitigate overfitting. Moreover, because of the additive nature of convolutions, a parallel layout often delivers faster inference than a serial design. Therefore, UniU-Net incorporates an auxiliary encoder during  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Vegetation Index** & **Formula** \\ \hline RVI & \(B_{NIR}/B_{R}\) \\ DVI & \(B_{NIR}-B_{R}\) \\ MSAVI & \(\frac{1}{2}\bigg{[}\left(2B_{NIR}+1\right)-\sqrt{\left(2B_{NIR}+1\right)^{2}-8 \left(B_{NIR}-B_{R}\right)}\bigg{]}\) \\ \hline \(\overline{B}_{NIR}\), \(B_{R}\) represent the spectral NIR band and red band. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formulas for vegetation indices.  
Figure 2: Architecture of UniU-Net.  
the encoding phase to further enhance the model's capacity to learn area-related features. This auxiliary encoder maintains the same module design and layer depth as the primary encoder, ensuring a balanced structure while providing complementary feature extraction.  
The convolutional module in UniU-Net follows the standard U-Net convolutional block design, using a kernel size of \(3\times 3\). The calculation process of convolution is as shown in Equation (1), where \(y_{ij}\) represents the output result of the convolution, and \(f_{k}\) denotes the convolution operation with a kernel size of \(k\). Recognizing that a binary classification task such as area extraction is prone to overfitting, dropout layers have been added to the convolutional modules. Moreover, the conventional ReLU activation has been replaced by LeakyReLU in order to mitigate potential issues with vanishing or exploding gradients during training. The structural combination of the Conv-block is shown in Figure 3a.  
\[y_{ij}=f_{k}\big{(}\{X_{i+\delta i,j+\delta j}\}\big{)} \tag{1}\]  
In the decoding phase, a standard U-Net uses skip connections to concatenate multi-scale features from the encoding phase with upsampled features, thereby enhancing the recovery of image details. Numerous studies have validated the effectiveness of these skip connections. Accordingly, UniU-Net retains this strategy. However, at each hierarchical level in the encoding phase, both the primary encoder and the auxiliary encoder produce intermediate feature maps of identical dimensions. Determining which of these features should be used--and in which proportions--becomes crucial for the final result. To address this, UniU-Net introduces a unified attention fusion module (UAFM) that assigns weights to the respective feature maps of the primary and auxiliary encoders during decoding. A detailed explanation of this module can be found in Section 3.4. Ultimately, by applying convolutional and upsampling operations, the decoder restores the features to the original input size and yields per-pixel classification results.