# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 3 Methods
### Network Backbone  
In the backbone, we use an architecture DRN composed of dilated convolution and residual network as the encoder for feature extraction. The residual network achieves excellent performance on image classification tasks through structures such as multiple convolutional layer cascades and residual linking. The residual network achieves excellent performance on image classification tasks through structures such as numerous convolutional layer cascades and residual connections. Compared with the ordinary network, the residual network causes less information loss and a better learning effect and has become the preferred feature extraction tool for various neural networks. However, the residual network inevitably gradually reduces the resolution inside the convolutional network so that some spatial structures are no longer easy to distinguish. While image segmentation is a pixel-level image classification task, the loss of spatial acuity reduces the accuracy of image segmentation. DRN increases the receptive field of higher layers by setting dilated convolution to compensate for the reduction in the receptive field caused by replacing some down-sampling in the residual network. DRN enables the convolutional network to ensure higher resolution without changing the receptive field. Moreover, DRN removes some of the maximum pooling in the residual network that is no longer necessary and the residual connection at the back of the network. It improves the accuracy of image segmentation without changing the depth and complexity of the model. There are several variants of DRN. Specifically, the structure we use is DRN-D-54. The structure diagram of the network is shown in Figure 3. We use a BatchNorm layer and a ReLU activation layer for each convolutional layer, forming a Conv-BN-ReLU group. DRN-D-54 is designed with three ordinary convolutional layers at the front of the network. In the middle of the network, similar to ResNet, DRN-D-54 has many bottleneck layers, some of which use dilated convolution. The bottleneck structure contains convolution with a kernel size of \(1\times 1\), which can change the network dimension more flexibly and reduce  
Figure 2: The structure of the semantic segmentation network for spacecraft images. the computation of the network [17]. Depending on whether the number of channels varies, the DRN-D-54 design has two different bottleneck layers. When the number of channels in the bottleneck layer changes, additional convolutional layers are connected at the residual connection. At the back of the network, there is a dilated convolutional layer and a standard convolutional layer. The low-level features have only passed through 12 convolution layers, while the high-level features pass through all convolution layers in the DRN-D-54 network. Dilated convolution improves the resolution of the output feature map without reducing the receptive field of individual convolution kernels, which has been proved to improve the segmentation performance in many segmentation networks. However, not all the convolutions can be replaced by dilated convolutions to achieve good results because dilated convolution may lead to gridding artifacts. As shown in Figure 4, assuming that the original image has only a point pixel, there are nine discrete pixel blocks in the feature map after dilated convolution. This gridding artifacts phenomenon will make the feature map relatively rough, showing a fine point-like distribution so that the segmentation results are not fine enough. Especially in the case of noise in the image, it will greatly affect the final segmentation result. Because there is a large amount of dilated convolution in Xception-65, there is noise in our data. In experiments, Xception-65 is not easier to converge, and it performs worse segmentation than many shallower networks or networks without dilated convolution. In order to remove the negative effects of these gridding artifacts, the degridding operation must be performed in the network.