# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 2 Materials and Methods
### Encoder Network
#### 2.2.2 Atrous Spatial Pyramid Pooling (ASPP)  
Remote-sensing images can present road information in varying widths and scales, which can result in the loss of image semantic information during the encoder stage. The ASPP method is capable of capturing contextual information at different scales and features, thereby reducing the loss of semantic information at different scales. It also enhances road perception, allowing the network to better adapt to road information of different widths and narrows. Moreover, ASPP helps to extract key features in road images, including texture, shape, and edge information and improves road semantic segmentation accuracy. ASPP improves the clarity and detail retention of road boundaries by varying the size of the dilation rate, which in turn suppresses the void effect and avoids transition smoothing or detail loss. The more comprehensive and accurate feature information is obtained through the feature pyramid, thus improving the classification accuracy of the target. Therefore, in this network, two sets of ASPP structures with different dilation rates are added to refine the semantic information extracted in the coding area.  
ASPP (atrous spatial pyramid pooling) was first introduced in DeepLab V2 and has since been refined and enhanced in subsequent versions. The underlying concept involves modifying the receptive field size by increasing the convolution rate, which enables the extraction of feature information at multiple scales.  
In convolutional operations, when the dilation rate is set to 1, the field size is 3 \(\times\) 3, which is considered normal. However, if the dilation rate is increased to 2 or 4, the field size increases to 7 \(\times\) 7 and 15 \(\times\) 15, respectively, as shown in Figure 3a-c.  
The two ASPP structures are almost identical, with the only difference being the size of the dilation rate used. Each ASPP structure includes three sets of empty convolutions, one set of maximum pooling, one set of 1 \(\times\) 1 convolutions, and three sets of average pooling.  
Figure 3: Sensory field size at different dilation rates. (a): rate = 1, (b): rate = 2, (c): rate = 4.  
The average pooling sets use convolution kernel sizes of 1 \(\times\) 1, 2 \(\times\) 2, and 3 \(\times\) 3, which effectively reduces the number of convolution layers. As shown in Figure 4:  
The ASPP structure consists of three groups of expansion convolutions, each with a different dilation rate. The convolution kernels used in each group are all 3 \(\times\) 3 in size. The dilation rates, denoted as a, b, and c, are shown in the figure in increasing order (a < b < c). The pool layer consists of 3 groups of average pooling, namely 1 \(\times\) 1 pooling, 2 \(\times\) 2 pooling, and 3 \(\times\) 3 pooling. The ASPP structure differs from the traditional fully connected layer pooling method by utilizing hole convolution and pooling operations. This approach effectively reduces the number of parameters, leading to improved computational efficiency and generalization ability of the model.