# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 2 Related Work  
Remote sensing image retrieval has always been the focus of remote sensing imagery. In early research works, many methods primarily used annotated labels to find similar images. However, since the labels of images could not fully express their contents, this approach often gives imprecise retrieval results. In contrast, content-based and semantic-based image retrieval methods have significantly improved retrieval performance. A content-based remote sensing image retrieval (CBRSIR) approach generally starts with extracting features from remote sensing images before calculating the retrieval results based on visual feature similarity. In the feature extraction stage, the following two main methods are available: manual feature extraction and deep-learning-based feature extraction. Regarding manual feature extraction CBIR methods, [PERSON] et al. [9] used the spectral features of remote sensing images, [PERSON] [10] employed a scale-invariant feature transform (SIFT) to describe the boundaries or contour information of geographic objects, and [PERSON] et al. [11] utilized shape features to extract the binary bitmap features of remote sensing images for image retrieval. Manual feature extraction is a time-consuming and highly specialized method, and more critically, it may overlook less intuitive or hidden image features. In recent years, deep-learning-based feature extraction techniques, represented by convolutional neural networks (CNNs), have presented an excellent performance in image processing. The intermediate outputs of CNN models encompass both the low-level details and high-level semantic information of images, providing a comprehensive visual content extraction. Generally, deep-learning-based feature extraction models consist of convolutional layers for local feature extraction, pooling layers for feature fusion, and fully connected layers reflecting global image features. [PERSON] et al. [12] investigated the performance of the features extracted by common CNNs and various aggregation methods for remote sensing image retrieval. [PERSON] et al. [13] integrated local and global features into one single model to retrieve candidate sets using global descriptors and further reorder them using local feature matching. [PERSON] et al. [14] developed a remote sensing image retrieval method using a multilayer feature fusion mechanism. They first extracted middle-layer and high-layer features from remote sensing images with various input sizes using VGG16 and GoogLeNet, respectively. Then, they separately fused the middle-layer and high-layer features through feature transformation, feature addition, and feature aggregation, finally fusing the middle-layer and high-layer features based on adaptive similarity weights. [PERSON] et al. [15] introduced a triplet network architecture for CNN feature extraction networks based on metric learning principles and triplet loss functions to effectively enhance the performance of remote sensing image retrieval. [PERSON] et al. [16] addressed the problem of poor retrieval performance due to the inability of single image features to fully reflect global image information by developing a remote sensing image retrieval method that adaptively fused multiple features using regression CNNs. Specifically, they used RESTNet and VGG16 to extract image features, calculated correlation coefficient matrices, predicted weights for each feature using regression CNNs, and obtained query classes. Finally, they performed a result ranking based on the distance between the queried image and query classes. Reference [3] also constructed a query image class composed of multiple images similar to the query image and strengthened query image features with the common features of images in the query image class; the experimental results demonstrated that this approach could improve retrieval accuracy.