## 6 Results
### Datasets  
The visible-light images in this study are sourced from a subset of the MTARSI dataset [39]. MTARSI, a fine-grained remote sensing aircraft dataset from Hefei University, contains 9385 remote sensing images covering 20 aircraft types, including A-10, B-1, B-2, C-130, and others. Annotated by seven remote sensing domain experts, the dataset aggregates data from 36 airports across countries such as the United States, United Kingdom, Japan, Russia, and China, collected under varying time, seasonal, and imaging conditions. For SAR images, the publicly available SAR-ACD dataset is adopted [40]. SAR-ACD includes six categories of civilian aircraft (e.g., A220, A320/321, A330, ARJ21, Boeing 737, and Boeing 787) and 14 categories of military/special-purpose aircraft, with a spatial resolution of 1 m [41]. Constructed from time-series SAR images using HH polarization, the dataset covers multiple aviation hubs such as Shanghai Hongqiao International Airport. Annotation was performed manually through expert visual interpretation combined with corresponding optical imagery. After data augmentation, the public SAR-ACD subset contains 3032 images of civilian aircraft (approximately 500 per category), featuring high-quality labels for radar echo characteristics across multiple phases (e.g., takeoff, taxiing, and parking). By performing multi-scale reconstruction on both datasets, a cross-modal image set with five resolution levels (0.1 m, 0.3 m, 0.5 m, 1 m, and 2 m) is generated. After sample balancing, a final dataset of 280 images is created, with representative examples shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(T\) & 180 s & \(\eta_{T}\) & 0.8 \\ \(M\) & 6 & \(\eta_{R}\) & 0.8 \\ \(G_{\mathrm{T}X}\) & \(10^{5}\) & \(N\) & 130 \\ \(G_{\mathrm{R}X}\) & \(10^{5}\) & \(K\) & 1 s \\ \(F_{\mathrm{r}min}\) & 6 dB & \(H_{S}\) & 656 km \\ \(B_{S}\) & 500 MHz & \(P_{S}\) & 1500 W \\ \(\sigma_{S}^{2}\) & \(5\times 10^{-20}\) W/Hz & \(F\) & 280 \\ C\({}_{S}\) & 300 MB & \(C_{f}\) & 100 MB \\ \(v_{a}\) & \(3\times 10^{8}\) m/s & \(v_{b}\) & \(3\times 10^{8}\) m/s \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Hyperparameters** & **Value** \\ \hline \(\lambda\) & 0.95 \\ Gain & 0.01 \\ Layer N & 2 \\ Hidden size & 256 \\ Episode length & 30 \\ \(lr_{a}\) & \(1\times 10^{-4}\) \\ \(lr_{c}\) & \(5\times 10^{-4}\) \\ PPO epoch & 5 \\ Clip param & 0.2 \\ Gamma & 0.99 \\ \hline \end{tabular}
\end{table}
Table 2: Hyperparameters in R-MAPPO.  
In this study, remote sensing content is generated based on the latitude and longitude positions of remote sensing satellites at different time intervals. Specifically, a square area is generated centered on the satellite's latitude and longitude coordinates \((longitude_{s_{i}},latitude_{s_{i}})\), with the top-left corner at \((longitude_{s_{i}}-0.5,latitude_{s_{i}}+0.5)\) and the bottom-right corner at \((longitude_{s_{i}}+0.5,latitude_{s_{i}}-0.5)\). The study involves six users, each having differentiated requirements, where all users only receive remote sensing content within their regions of interest (ROIs, as shown in Figure 4). During the simulation period, a total of 280 remote sensing content items and 547 user requests are generated.  
Figure 4: Interest area for all users.  
Figure 3: Sample images with different resolutions from the experimental dataset.