# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 2 Materials and Methods
### Decoder Network  
In a semantic segmentation network, the decoder plays a crucial role in upscaling the feature map generated by the encoder and classifying each pixel point. The decoder network performs multi-scale feature fusion and feature reconstruction through upsampling to accurately predict the pixel-level segmentation of the original image, thereby improving the overall segmentation accuracy of the image. The bilinear interpolation method is commonly used for upsampling; however, it may not be suitable for images that contain rich features and detailed information, such as remote-sensing images. Bilinear interpolation involves taking a weighted average of adjacent pixels, which is unable to capture the intricate texture information present in the image. This can result in a poorly detailed image after upsampling. In the context of image processing, the bilinear interpolation method may not be suitable for upsampling due to its sensitivity to noise. In such cases, the decoder part of the SegNet network structure can be used as an alternative in DANet. The upsampling method employed in the decoder is deconvolution, which is known to provide superior feature reconstruction and is less computationally demanding than traditional methods. Deconvolution outperforms other interpolation methods in terms of accurately restoring the detailed information of the original image and providing finer upsampling results. Additionally, deconvolution utilizes a convolution kernel to conduct convolution operations with the feature map during upsampling, enabling the model to take into account local spatial and contextual information. By merging features of adjacent pixels during the convolution process, deconvolution can more effectively preserve and recover local detail information such as target shape, edges, and texture. In the field of  
Figure 4: The structure of ASPP.  
remote-sensing image road semantic segmentation, the use of deconvolution can result in a more accurate reconstruction of road features while retaining their fine shape, edge, and texture features. This approach is particularly useful for identifying fine and narrow roads in remote-sensing images and can effectively reduce the jaggedness of segmentation edges. By linking the contexts, this method can also ensure consistency and continuity in the identification of road information.  
SegNet's decoder is responsible for restoring low-resolution feature maps to the resolution of the original input image by upsampling. DANet has chosen SegNet as its decoder due to its ability to preserve location information by storing encoder stage indexes. During the decoder stage, these indexes are utilized to select the most relevant feature mappings for upsampling. This feature mapping multiplexing mechanism helps to preserve more detailed information, ultimately improving the accuracy of segmentation results.  
The decoder network comprises four main components, each containing an upsampling layer, a convolutional layer, a BN layer, and a ReLu activation function. The input image is initially upsampled and then undergoes feature reduction through the BN layer, ReLu activation function, and convolutional layer. After four upsampling processes, an image of the same size as the original image is obtained. The encoder loses more semantic information in the feature map due to multiple convolutions and downsampling, making it difficult to perfectly restore the original image information through upsampling alone. Therefore, several convolution layers are utilized in each upsampling process. As shown in Figure 5:  
Before being passed through the decoder, the feature map is pooled to adjust its size for smoother entry into the decoder network. However, during the training process, the deep neural network becomes increasingly difficult to train and slower to converge as the network depth increases. This problem can be effectively addressed by incorporating a BN layer, which speeds up convergence and simplifies network training.