# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 3 Remote Sensing Image Retrieval Algorithms Based on Scene Graph Similarity  
### Problem Description  
In marine management scenarios, public reporting is an important approach for marine management departments in monitoring illegal activities such as land reclamation and sea filling [33]. The similarity between publicly reported images and those in remote sensing image databases could be determined using remote sensing image retrieval algorithms, allowing for the rapid retrieval of similar images in databases, thereby improving marine management efficiency [34]. Publicly reported images served as the query image \(\mathrm{Iq}\), and the remote sensing image database Gallary was considered as the set of images to be searched, as illustrated in Figure 2. The developed image retrieval algorithm determined the similarity of the publicly reported images with all images in the remote sensing database, and the results were obtained based on the similarity order [35]. Typically, traditional content-based image retrieval algorithms extract features from two input images and then calculate their similarity based on feature vectors. However, direct feature extraction from remote sensing images cannot acquire the semantic information in the images, which affects image retrieval accuracy. Therefore, this research proposed a remote sensing image retrieval algorithm based on scene graph similarity. The challenges of this approach include the following:  
1. The basic task for extracting semantic information from images is object detection. Traditional object detection tasks possess predefined sets of target object categories to classify and locate objects within images. However, target object categories in publicly reported images, i.e., query image \(\mathrm{Iq}\), are often unpredictable, making it a typical one-shot object detection task. 2. A scene graph is a structured representation of the scene in an image to clearly express the objects, attributes, and relationships among the content in the scene. Remote sensing images are significantly different from ordinary images, primarily due to their overhead perspective; therefore, relationships among objects are mainly represented as spatial relationships on a plane. Secondly, the field of view is large, thus the images include several targets and the complex spatial relationships among them. This poses challenges for constructing scene graphs for remote sensing images. To the best of our knowledge, there is currently a lack of methods for constructing scene graphs specifically for remote sensing images in the academic community. To address challenge (1), this research presented a one-shot object detection algorithm for remote sensing images based on Siamese networks. For challenge (2), it was proposed to obtain the category information of the targets based on the detection results. Building on this, a graph neural network-based scene graph similarity computation module was introduced, employing a neural tensor network to perform the similarity calculations. Finally, a scene graph similarity-based remote sensing image retrieval algorithm was formed, as illustrated in Figure 2:  
In the Figure 2, Iq denotes the query image, and Gallery denotes the collection of n remote sensing images to be retrieved with Is, Is\(\in\)Gallary. Firstly, a one-shot object detection algorithm was used to extract the target information from both the query image Iq and the image Is in Gallary. Therefore, using a scene graph construction algorithm, the target information from Iq and Is was transformed into node (L, x, y, w, h) and edge (d, \(\theta\)) information within the scene graph Gq and Gs. Finally, a scene graph similarity calculation algorithm was employed to calculate the similarity between the original two input remote  
Figure 2: The developed remote sensing image retrieval algorithm. sensing images. This process included a scene graph feature extraction module and a feature similarity calculation module.