## 3 Experimental Results and Analysis
### Experimental Analysis
#### 3.3.5. Experimental Analysis and Comparison  
SPEED provided by SPEC2019 does not provide the test dataset label, so relevant tests cannot be completed on the test dataset. In this section, the training dataset of SPEED is divided into six equal parts, among which five groups are used as training datasets and one group is used as the test dataset. Six groups of data, A-F, were cross-combined to complete relevant experiments and prove the robustness of the model. The default parameters of the experiment are as follows: the epoch size was 300, the batch size was 16, and the image input size was 224 \(\times\) 224 \(\times\) 3. Meanwhile, we chose other methods for comparison experiments, as shown in Table 7. First, the six groups of experiments, A-F, were completed, as shown in Table 8, and the mean values of the six groups of experiments were calculated. According to the relative distance, the error distribution of the six groups of experiments A to F was drawn, as shown in Figure 8. The translation error fluctuated in a large range after 20 m, and the orientation error distribution was relatively uniform. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & \(\mathbf{E_{T}}\) **(m)** & \(\mathbf{E_{R}}\) **(deg)** \\ \hline Dual-T1 & 0.04430 & 1.54057 \\ Dual-T2 & 0.04239 & 1.67587 \\ Dual-T3 & 0.04288 & 1.61775 \\ Single-T & 1.1948 & 2.1788 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of single-channel and dual-channel results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Estimating Process** \\ \hline TfNet [28] & SLN + LRN + PnP solver \\ SPN [42] & SLN + End-to-end learning \\ LSPnet [6] & End-to-end learning \\ Ours & End-to-end learning \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Flow of various satellite pose estimation methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **Translation Error** & **Orientation Error** \\ \hline Resnet50 & 0.06325 & 2.53624 \\ EfficientNetB0 & 0.04205 & 1.52393 \\ EfficientNetB1 & 0.04853 & 1.50589 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pose estimation accuracy for the different backbone. Figure 8: Pose errors distributed by object distance. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **Mean** & \begin{tabular}{c} **LSPnet** \\ **[**6**]** \\ \end{tabular} & \
\begin{tabular}{c} **SPN** \\ **[**42**]** \\ \end{tabular} &
\begin{tabular}{c} **TFNet** \\ **[**28**]** \\ \end{tabular} \\ \hline Mean \(E_{R}\) (deg) & 1.564 & 1.597 & 1.586 & 1.596 & 1.542 & 1.550 & 1.573 & 15.70 & 8.425 & 0.969 \\ Median \(E_{R}\) (deg) & 1.499 & 1.497 & 1.516 & 1.542 & 1.449 & 1.460 & 1.494 & - & 7.069 & 0.801 \\ Mean \(E_{T}\) (m) & 0.042 & 0.044 & 0.043 & 0.045 & 0.044 & 0.043 & 0.044 & - & - & 0.006 \\ Median \(E_{T}\) (m) & 0.036 & 0.035 & 0.037 & 0.037 & 0.037 & 0.037 & 0.037 & - & - & 0.005 \\ Mean \(\tilde{g}_{T}\) (m) & 0.501 & 0.538 & 0.515 & 0.528 & 0.522 & 0.524 & 0.521 & 0.519 & 0.294 & - \\ Median \(\tilde{g}_{T}\) (m) & 0.309 & 0.321 & 0.312 & 0.319 & 0.304 & 0.323 & 0.315 & - & 0.180 & - \\ \(T\) (ms) & 36.82 & 37.46 & 36.64 & 36.89 & 37.25 & 37.98 & 37.17 & - & - & 212.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Analysis and comparison of test results. As shown in Table 7, LSPnet [6], SPN [42], and TfNet [28] were selected for comparison in this paper. LSPnet is composed of three interconnected convolutional neural networks named Position, Localization, and Orientation. It is an advanced end-to-end approach. SPN adopts perspective sampling discretization, describes the prediction of rotation as a classification problem and an offset regression problem, and combines 2D detection results to solve the translation amount. This article does not provide inference speed, but adds object detection in the pose estimation, which increases the reasoning complexity and time consumption.