# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 3 Remote Sensing Image Retrieval Algorithms Based on Scene Graph Similarity
### One-Shot Object Detection Algorithm for Remote Sensing Images Based on a Siamese Network  
Based on the characteristics of remote sensing image data and marine management requirements, one-shot object detection should be implemented in the context of remote sensing image retrieval. This entails detecting objects in retrieval images belonging to the same category as the objects in the query image. In a one-shot object detection task, a new class has only one sample, and detection is performed using only base-class data without fine-tuning for new class objects. To address this problem, this research proposed a one-shot object detection algorithm based on a Siamese network [36] and asymmetric convolution networks, called SiamACDet. The overall process is presented in Figure 3.  
The input consisted of a retrieval image and a query image. Firstly, both images were passed through a shared-weight feature extraction network, ACSENet, to obtain feature maps. Then, the images and their feature maps were input into an attention-based region proposal network (ARPN) [37] to generate candidate regions (proposals). Finally, the proposals along with the feature maps of the images were fed into a dual-branch detector to obtain the final category and position information, as shown in the red box. (1) ACSE residual module based on asymmetric convolution  
Due to the complex background characteristics of remote sensing images, traditional residual networks present weaker feature extraction capabilities for these images. To enhance the ability of the network to extract features related to targets in remote sensing images, this research replaced standard convolutions in the residual module with asymmetric convolution (AC) structures [38]. Furthermore, considering that standard convolutional layers perform only convolution operations on individual channels without addressing inter-channel dependencies and neglect correlated information among channels, this research introduced a channel attention mechanism known as squeeze and excitation (SE) [39] into the AC residual module. The SE module dynamically adjusted the importance of each channel to more accurately capture crucial features within the input feature maps. The asymmetric convolution-based AC module, as depicted in Figure 4b, modified the original residual module by replacing the 3 \(\times\) 3 convolution kernel traditionally combined with BN and ReLU layers with a structure that integrated 3 \(\times\) 3, 1 \(\times\) 3, and 3 \(\times\) 1 convolution kernels, each combined with BN and ReLU layers, and summed their outputs. This  
Figure 3: The schematic diagram of the SiamACDet algorithm. asymmetric convolution structure enhanced the representational capacity of the standard square convolution kernel by introducing two additional asymmetric convolution kernels, one horizontal and one vertical, to its skeletal structure. Hence, the developed asymmetric convolution structure improved the effectiveness of feature extraction. The ACSE residual module with the added attention mechanism, as illustrated in Figure 5, included a channel-wise multiplication operation denoted as V. Building upon the AC module, the process started with global pooling, followed by two fully connected layers with sigmoid activation functions to obtain the weight values. These weights were then multiplied channel-wise with the original image. (2) ACSENet feature extraction network based on asymmetric convolution  
ResNet101 is a typical architecture within the deep residual network ResNet, comprising 101 convolutional and pooling operations. To solve the problem of detecting small objects in remote sensing images, in this chapter, ResNet101 was modified by replacing the standard residual modules with ACSE modules from the second to fifth layers, adopting a feature pyramid network [40] (FPN) structure. The FPN-structure-based feature extraction network ACSENet is presented in Figure 6. Initially, 1 \(\times\) 1 convolution operations were applied to feature the maps obtained from the second to fifth layers.