# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation
### Evaluation of the One-Shot Object Detection Algorithm for Remote Sensing Images
#### 4.2.2 Performance Comparison Result  
To evaluate the performance of the developed model, this research conducted comparative experiments on the DIOR and RSSG datasets, comparing them with other one-shot object detection algorithms, CoAE [45], AIT [46], and BHRL [47]. A momentum stochastic gradient descent (Momentum SGD) optimizer [48] with an initial learning rate of \(1.00\times 10^{-3}\), momentum parameter of 0.9, weight decay coefficient of \(1.00\times 10^{-4}\), and batch size of 2 during training was employed. In the DIOR dataset, categories such as train stations and chimneys were set as invisible classes, while those such as oil tanks and ships were set as visible classes, totaling 12 categories. In the RSSG dataset, harbor and oil tank categories were set as visible classes, and ship, sea field, and factory categories were set as invisible classes. The experiments involved training the visible classes and testing separately the visible and invisible classes. The experimental results obtained for the DIOR and RSSG datasets are summarized in Tables 3 and 4, respectively. Based on Tables 3 and 4, the proposed SiamACDet algorithm performed well in one-shot object detection tasks for remote sensing images. On the DIOR dataset, compared to the previous methods CoAE, AIT, and BHRL, the proposed algorithm improved the mAP values for visible classes by 3.9%, 2.9%, and 1.8%, and for invisible classes, by 2.5%, 1.8%, and 1.1%, respectively. On the RSSG dataset, compared to CoAE, AIT, and BHRL, the proposed algorithm showed improvements in the mAP values for visible classes by 3.6%, 1.8%, and 1.1%, and for invisible classes, by 1.9%, 1.3%, and 0.2%, respectively. Compared to previous methods, this study introduced asymmetric convolutions and attention mechanisms into the ResNet-based feature extraction network, proposing the ACSENet feature extraction network. The asymmetric convolution structure enhances the representational capability of standard square convolution kernels, allowing for the better extraction of deep features in remote sensing images. This addresses the problem of the weakened feature extraction ability and reduced detection accuracy caused by the complex backgrounds of remote sensing images. The FPN structure is incorporated into the ACSENet feature extraction network to improve the detection accuracy of small objects in remote sensing images. An ARPN candidate region generation network based on an attention mechanism is designed to replace the traditional RPN network. This network \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Visible Classes**} & \multicolumn{3}{c}{**Invisible Classes**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Farming**} & \multirow{2}{*}{**Oil Tank**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**Sea Fields**} & \multirow{2}{*}{**Boat**} & \multirow{2}{*}{**House**} & \multirow{2}{*}{**mAP**} \\  & \multicolumn{2}{c}{**Floating Rafits**} & & & & & & \\ \hline CoAE [17] & 8.1 & 30.6 & **19.4** & 2.6 & 8.5 & 36.3 & **15.8** \\ \hline AIT [18] & 9.6 & 32.7 & **21.2** & 2.9 & 9.6 & 36.2 & **16.2** \\ \hline BHRL [19] & 10.1 & 33.6 & **21.9** & 3.1 & 10 & 39.4 & **17.5** \\ \hline SiamACDet & 10.5 & 35.4 & **23** & 3.5 & 10.2 & 39.4 & **17.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the comparison experiment on the RSSG dataset (%).