# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Dataset  
Currently, there are very few datasets that can be used for spacecraft image segmentation. We used the photorealistic spacecraft images provided in SPEED [9] and URSO [4], annotated by the LabelMe tool, and obtain fine masks of spacecraft targets. Based on this, we established a spacecraft segmentation dataset. The dataset contains three types of spacecraft, Tango, Dragon, and Soyuz. The appearance of each spacecraft is shown in Figure 8 below. All three spacecraft are morphologically different and representative. Choosing these three types of spacecraft as the dataset for training, rather than a single spacecraft, improves the variety of objects in our dataset and the difficulty of network learning while allowing us to demonstrate better generalization of our network. Figure 8: The appearance diagram of three types of spacecraft. During the labelling process, we marked different types of spacecraft with different colors. In addition, three objects are represented by the category names of Spacecraft A, Spacecraft B, and Spacecraft C. Their categories and color settings are shown in Table 2 below. The dataset consists of 600 monocular images with image sizes of 1280 \(\times\) 960 and 1920 \(\times\) 1200. Spacecraft A and Spacecraft B each have 150 images in the dataset, and Spacecraft C has 300 images. Since the morphology of Spacecraft C is more complex and often asymmetrical in the image, a larger amount of data were specially set for Spacecraft C. The effect of image annotation is shown in Figure 9 below. We divided the dataset into the training set, test set, and validation set in a 6:2:2 ratio. Finally, there are 360 training set images, 120 test set images, and 120 validation set images. The ratio of the three object categories in each subset is 1:1:2, which maintains the same data distribution. Among them, the training set and the validation set participate in the training process of the model. The validation set is used to avoid training overfitting and to determine the learning rate during training. We performed mask prediction and evaluation metrics on the test set to evaluate the training results of different models. Meanwhile, we used the resize operation to set the image size to 512 \(\times\) 512 pixels in the network. In remote sensing datasets, the size of the same type of object does not change much, so many deep learning models are anchor-based networks. The network can converge faster. However, even if it is the same object in the spacecraft picture, the size of the object may vary greatly due to different distances, so the spacecraft object has multi-scale characteristics. In addition, compared with the targets in the KITTI dataset, the spacecraft objects may appear in the image in any pose because of their motion characteristics. If the spacecraft structure is complex, then the changes reflected in the image may be more, which is the multi-attitude characteristics of the spacecraft data. The background of the spacecraft is space, so the image will inevitably be affected by the illumination generated or reflected by various stars and spacecraft, resulting in large noise. Real data may even be much noisier than synthetic data. Spacecraft A is derived from the SPEED [9]. In order to improve the realism of the synthetic data, all images of Spacecraft A have been subjected to Gaussian blur processing (\(\sigma=1\)) and zero mean, Gaussian white \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Spacecraft** & **Category Name** & **RGB Value** & **Color** \\ \hline
1 & Tango & Spacecraft A & (128, 0, 0) & \\
2 & Dragon & Spacecraft B & (0, 128, 0) & \\
3 & Soyuz & Spacecraft C & (128, 128, 0) & \\
4 & – & Background & (0, 0, 0) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification number, name, and color table. Figure 9: Diagram of the dataset. The first column is the original image, and the second column is the image displayed in an overlapping form after annotation. noise processing with (\(\sigma^{2}=0.0022\)).