# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 2 Related Work
### Semantic Segmentation Based on Deep Learning  
Since the advent of AlexNet [37], deep neural networks have begun to dominate image classification tasks and have shown excellent classification performance. FCN [38] adapted the classification network to a fully convolutional network and made significant progress by applying the network to semantic segmentation by classifying pixels. In order to enhance contextual information aggregation to improve the effect of semantic segmentation, some variants of FCN-based models have been proposed. SegNet [39] adopts an encoder-decoder structure that utilizes low-level information to help refine segmentation masks. U-Net [15] concatenates the outputs of low-level layers with those of high-level layers for information fusion. DeepLab [40] and CRF-RNN [41] use the conditional random field as post-processing for structure prediction in scene parsing. DPN [42] implements semantic segmentation using Markov random fields. PSPNet [43] builds a pyramid structure and fuses middle-level and high-level semantic features to obtain multi-scale context information.  
In semantic segmentation tasks, contextual information plays an important role in image understanding to improve segmentation quality. Dilated convolutions can increase the receptive field without losing information by inserting cavities of different rates into regular convolutions. Deeplabv2 [44] and Deeplabv3 [45] embed contextual information using an ASPP, which uses parallel dilated convolution with different rates to fuse multi-scale information and expand the receptive field. DeepLabv3+ [13] adds a decoder structure while using the Xception with depthwise separable convolution as the backbone. It refines the segmentation results and makes the boundary of the object segmented more clearly. [PERSON] et al. [46] proposed using recurrent neural networks to retrieve correlations in the global space.  
In recent years, attention mechanisms have begun to be applied to deep learning tasks. From machine translation to image classification, attention mechanisms have demonstrated excellent performance. The self-attention mechanism [47] has been pioneered to achieve good results in machine translation by extracting the global dependencies of the input. SENet [20] proposes a lightweight channel attention mechanism that adaptively recalibrates the channel feature maps by establishing interdependencies between channels through the \"Squeeze-and-Excitation\" (SE) block. BAM [48] and CBAM [49] design attention mechanisms for both channel and spatial dimensions in a similar way to achieve adaptive feature refinement. PSANet [50] makes the network segmentation mask more sensitive to position and category information by the point-wise spatial attention module to adaptively aggregate contextual information for each point. DANet [51] expanded the self-attention mechanism in the segmentation task. It utilizes two attention mechanisms, which combine adaptive local features with their global dependencies to capture rich contextual relations. SKNet [52] designed a selective kernel unit that fuses the effective receptive fields of neurons of different sizes through branches of different kernel sizes. EMANet [53] proposed to describe the attention mechanism as an expectation-maximization method using the expectation-maximization attention module to perform mask estimation iteratively. Table 1 below shows a comparison of our proposed approach with several related methods. The first line indicates: whether to use the segmentation method, whether to adopt a deep learning strategy, whether the network design is carried out according to the characteristics of the spacecraft object, and whether relevant datasets have been established.