# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
###### Abstract  
With the rapid development of remote sensing image data, the efficient retrieval of target images of interest has become an important issue in various applications including computer vision and remote sensing. This research addressed the low-accuracy problem in traditional content-based image retrieval algorithms, which largely rely on comparing entire image features without capturing sufficient semantic information. We proposed a scene graph similarity-based remote sensing image retrieval algorithm. Firstly, a one-shot object detection algorithm was designed for remote sensing images based on Siamese networks and tailored to the objects of an unknown class in the query image. Secondly, a scene graph construction algorithm was developed, based on the objects and their attributes and spatial relationships. Several construction strategies were designed based on different relationships, including full connections, random connections, nearest connections, star connections, or ring connections. Thirdly, by making full use of edge features for scene graph feature extraction, a graph feature extraction network was established based on edge features. Fourthly, a neural tensor network-based similarity calculation algorithm was designed for graph feature vectors to obtain image retrieval results. Fifthly, a dataset named remote sensing images with scene graphs (RSSG) was built for testing, which contained 929 remote sensing images with their corresponding scene graphs generated by the developed construction strategies. Finally, through performance comparison experiments with remote sensing image retrieval algorithms AMFMN, MiLaN, and AHCL, in precision rates, Precision@1 improved by 10%, 7.2%, and 5.2%, Precision@5 improved by 3%, 5%, and 1.7% and Precision@10 improved by 1.7%, 3%, and 0.6%. In recall rates, Recall@1 improved by 2.5%, 4.3%, and 1.3%; Recall@5 improved by 3.7%, 6.2%, and 2.1%; and Recall@10 improved by 4.4%, 7.7% and 1.6%. remote sensing image; scene graph; image retrieval; object detection; Siamese networks 4.0.0.0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-0000000-0000000-0000000-0000000-00000000-0000000000000000000000 Remote sensing image retrieval aims to find images within a remote sensing image database that are similar to query images. Essentially, it is an image similarity computation problem, which is a specific application of general image retrieval tasks combined with the characteristics of remote sensing images [3]. Image retrieval primarily includes two approaches: content-based image retrieval (CBIR) [4] and semantic-based image retrieval (SBIR) [5]. CBIR algorithms calculate similarity by acquiring the deep features of the entire image, lacking the ability to capture semantic information within the image, resulting in a low retrieval accuracy. When performing image retrieval, not only are the content features of the image considered but higher-level semantic information, such as the attributes and spatial relationships of target images, are also taken into account. The semantic-based approach to remote sensing image retrieval primarily extracts semantic information from remote sensing images and then uses it to calculate the similarity of remote sensing images to accurately obtain the query results. However, existing semantic-based remote sensing image retrieval methods primarily perform image retrieval through the attributes of the objects within images, neglecting the relationships among objects in remote sensing images. This omission prevents the model from fully utilizing the semantic information within images. CBIR and SBIR retrieval processes are described in the following example.