## 3 Proposed Superpixel-Based MSGATN  
### Overview of the Proposed MSGATN  
In this subsection, an overview of the proposed MSGATN is given briefly in Figure 1. Firstly, the difference intensity of bitemporal remote sensed images is obtained by Euclidean distance. Based on the pixel-wise similarity, the difference intensity map can be segmented to massive unstructured multi-scale superpixels of varied shapes and boundaries by simple linear iterative clustering (SLIC). With the segmented DI acquired, a region adjacency graph (RAG) can be constructed based on the mutual consistency of neighbor superpixels. The spatial-temporal relationships between these superpixels can be well modeled by the edges of constructed DI RAG. Then, the bitemporal remote sensing images are also segmented into superpixels with the guidance of the segmentation information extracted in DI superpixel segmentation. Several significant statistical characteristics, i.e., minimum, maximum, mean, standard deviation, skewness, and kurtosiscan further represent the features of multi-scale bitemporal superpixels. As a result, the input graph of graph attention network (GATN) can be constructed by the nodes obtained from features of bitemporal superpixels and the edges acquired in the RAG of DI superpixels. Finally, superpixel-level prediction is obtained by GATN and remapped to form the pixel-level change map. The detailed inference process of MSGATN can be illustrated in Algorithm 1.  
```
1:Input:\(T^{l}\), \(T^{2}\): the bitemporal images.
2:Begin
3:\(DI\gets Euclidean\_Distance(T1,T2)\); // obtain the difference intensity
4:\(S^{DI}\gets SLIC(DI)\); // conduct superpixel segmentation over \(DI\)
5:\(\mathcal{G}_{sp}\big{(}\mathcal{V},\mathcal{E}_{sp}\big{)}\gets RAG \big{(}S^{DI}\big{)}\); // acquire the region adjacency graph of \(S^{DI}\)
6:\(S^{T1}\gets superpixel\_segmentation(T1)\); // segment \(T1\) according to \(S^{DI}\)
7:\(S^{T2}\gets superpixel\_segmentation(T2)\); // segment \(T2\) according to \(S^{DI}\)
8:\(F^{1}\gets feature\_analyse(S^{T1})\); // represent the significant features of \(S^{T1}\)
9:\(F^{2}\gets feature\_analyse(S^{T2})\); // represent the significant features of \(S^{T2}\)
10:\(\mathcal{V}_{f}\leftarrow concatenate\big{(}F^{1},F^{2}\big{)}\); // collect the superpixel-level bitemporal features
11:\(\mathcal{G}_{input}\leftarrow\Big{(}\mathcal{V}_{f},\mathcal{E}_{sp}\Big{)}\); // construct the input graph for GATN
12:\(\mathcal{F}_{output}\leftarrow GATN\big{(}\mathcal{G}_{input}\big{)}\); // obtain superpixel-wise change map
13:\(CM\gets remap\big{(}\mathcal{F}_{output}\big{)}\); // remap the superpixel prediction to acquire final \(CM\)
```  
**Algorithm 1** Inference process of MSGATN  
Figure 1: The framework and procedure of MSGATN.  
As shown in the procedure above, the proposed MSGATN firstly obtains the multi-scale unstructured features of bitemporal remote sensed images through superpixel segmentation, which further promotes the fine-grained CD prediction. Then, the mutual relationships inside these bitemporal superpixels are well represented and modeled by GATN. Given the overall framework and inference process of the proposed MSGATN, the detailed information of the proposed graph construction mechanism can be given in the following section.