# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 1 Introduction
Researchers can build a spacecraft dataset with labelled information based on specific spacecraft application scenarios and train the network until the model converges. During inference, the network can quickly make predictions on the images and derive spacecraft segmentation results. However, space data are sensitive, and it is difficult to establish a real space object dataset. Some publicly available segmentation datasets, such as the Pascal Visual Object Classes (Pascal VOC), the Microsoft Common Objects in COntext (COCO), and the Cityscapes, are unsuitable for validating spacecraft segmentation algorithms [10; 11; 12]. The publicly available spacecraft datasets, such as Spacecraft Pose Estimation Dataset (SPEED) and the Unreal Rendered Spacecraft on Orbit (URSO), are mainly used to solve the attitude problem and lack the annotation of segmentation information [4; 9]. Therefore, based on SPEED and URSO, we selected many photorealistic spacecraft images for refined annotation and constructed a spacecraft image segmentation dataset. We do not use the same annotation method as the spacecraft component segmentation dataset established in Ref. [7]. Our research is more concerned with whether the entire spacecraft can be accurately segmented. At the same time, we distinguish different kinds of spacecraft in the dataset annotation. We use a semantic segmentation network to solve the problem of spacecraft image segmentation. We also design the internal structure model of a deep neural network. Inspired by DeepLabv3+ [13] and DenseASPP [14], we propose a dilated convolution-based multi-scale neural network for spacecraft image segmentation, which we call SISnet. The network uses DeepLabv3+ as the basic framework and adopts the encoding and decoding form proposed by U-Net [15]. We use the spacecraft image segmentation dataset we made as the experimental dataset to verify the segmentation ability of the network model. Specifically, in the backbone network part, our network model uses dilated residual networks (DRN) [16] as the encoder for deep feature extraction. Our encoder utilizes dilated convolution, enablingthe network to increase the receptive field while maintaining image resolution. Compared with residual networks [17], our backbone network removes the residual structure and skip-connections in the last few layers, which improves the gridding artifacts. Inspired by FarSee-Net [18] and [PERSON] et al. [19], we utilize two atrous space pyramid pooling (ASPP) modules in the network to form a parallel-ASPP structure. The high-level and low-level features extracted from the backbone network are fused in the parallel-ASPP structure to improve the segmentation effect of the model for multi-scale targets. In addition, inspired by SENet [20] and ECA-Net [21], we have added a Squeeze-and-Excitation Module (SEM) between the backbone network and the parallel-ASPP structure. SEM is a channel attention mechanism module, which enables the network to pay more attention to some feature maps by giving channel weights. The innovation of this paper is that, through dilated convolutional and the parallel-ASPP structure, our network enhances contextual information and mitigates the effects of gridding artifacts. At the same time, the channel attention mechanism is used to recalibrate the features, which improves the learning ability of the network. Our method is more robust to noise in the image and can segment more complete and clear spacecraft masks. We experimented with SISnet on the spacecraft image segmentation dataset we produced above and compared it with other deep learning neural network methods. We experimented with SISnet on a spacecraft image segmentation dataset we produced and compared it with other deep learning neural network approaches. Experiments show that our method achieves a higher level with better segmentation results. Our work and predicted mask effects are shown in Figure 1.  
The contributions of our paper are summarized as follows:  
1.