# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 2 Related Work
In addition to CNNs, graph convolutional networks (GCNs) [17] are increasingly applied in image retrieval tasks since they effectively model local structures using CNNs and explore the mutual dependencies between graph nodes. [PERSON] et al. [18] proposed a Siamese graph convolution network (SGCN) framework for the calculation of the similarity between two remote sensing images. The aim of the SGCN was to learn a latent representation space based on a region adjacency graph (RAG) for remote sensing images, where similar remote sensing images were close and dissimilar images were far. [PERSON] et al. [19] addressed issues such as the excessive aggregation of local area information in remote sensing images at a single node, which might lead to missing high-frequency information. They developed an edge attention mechanism to highlight the interactions among regions in remote sensing images to enhance our understanding of global image information and improve retrieval effectiveness. A semantic-based remote sensing image retrieval (SBRSIR) approach primarily generates high-level semantic information such as object or scene descriptions for remote sensing images and then calculates the similarity based on this semantic information. However, the challenge of this approach lies in generating comprehensive semantic descriptions that accurately represent the details of remote sensing images. The authors in [20; 21] focused on generating abstract titles for remote sensing images. However, due to the wide perspective of remote sensing images and the rich information of geographical features contained in these images, abstract titles might not adequately describe the detailed image information, making them unsuitable for precise image-to-image retrieval tasks in remote sensing. Generally, the detailed semantic information in images contains two main aspects: the objects present in the image and the relationships among these objects [22]. The academic community has employed techniques such as image classification, object detection, or visual relationship detection to solve semantic discovery problems [23]. Many solutions have been developed to model semantic information. [PERSON] [24] used knowledge graphs to structurally store the information about objects and their relationships in images. Other research works [25; 26; 27; 28] have further explored the application of ontology technology to improve the efficiency and accuracy of retrieval. In recent years, there has been a growing interest among researchers to employ scene graphs to represent image semantics, since scene graphs have been found to be more conducive to help in image retrieval. For instance, [PERSON] et al. [29] generated scene graphs based on the COCO-Stuff [30] dataset and trained a scene graph embedding network to obtain semantic-based structured image retrieval. [PERSON] et al. [31] used visual scene graphs (VSGs) and textual scene graphs (TSGs) simultaneously to construct and extract the visual relationships between any two objects in images. This multimodal approach helped evaluate image similarity from both the perspectives of content and semantics. [PERSON] et al. [32] applied scene graphs to model high-level semantic information in images and used GCNs to calculate scene graph similarity as a measure of original image similarity. The abovementioned studies primarily focused on general images. As previously discussed, remote sensing images have more distinct characteristics than general images, which necessitate different approaches for constructing scene graphs. Firstly, compared to general images, remote sensing imaging covers a wider field of view, resulting in a multitude of geographical features. Secondly, remote sensing images are captured from a top-down perspective, emphasizing planar spatial relationships among objects, including their relative or absolute positions with respect to a reference point. In contrast, scene graphs for general images may also involve dynamic relationships in addition to spatial relationships.