## 4 Remote Sensing Image Scene Retrieval
### Retrieval by Graph Models  
Unlike the aforementioned RSISR methods, which often concatenate features of all genres into one vector, the graph models enriched with node and edge attributes are usually the elective data structures for RSISR, in terms of scenes constitution and the relations among them. The high-resolution RS scenes can be precisely retrieved by graph models. Facing the problem caused by multi-type and large-quantity features, the graph models outperform the vector-based methods in that they make multiple complementary features effective.  
In this part, we should be aware that many existing region-based graph models have been utilized in RSISR. For example, [67; 68; 69; 70] used attributed relational graphs (ARG) to represent the images instead of using deep features, while their similarity metrics are region-based. These graph models are not what we are pursuing. Only graph-based retrieval is discussed below in this section, which means there is retrieval in the data set by graph-based algorithms. Each image in the data set corresponds to a node in a graph, and the edge between two nodes represents the connection between scenes, rather than regions. A weight on one edge represents the similarity. As seen in Figure 4, such a mesh structure shows that auxiliary information from other scenes in the dataset is actually taken into consideration, from vector-based one-to-one feature retrieval to one-to-many feature retrieval.  
Owing to the single-image feature vector-based algorithms, to alleviate the incredible difference of the retrieval accuracy among queries, a three-layer graph learning method was applied to holistic and semi-supervised local features [71], which is reasonable and scalable. Here, the graph-based algorithm of retrieval consists of a kernel matrix, which represents the similarity among images using multiple features. The paper in [72] also uses RS images to make a graph which is undirected (fully connected), and then utilizes graph learning for retrieval. Here, original rank scenes are also graph nodes, and the similarity between nodes is weighted by the resemblance between RS scenes under the  
Figure 4: Schematic diagrams of distance measures and graph-based measures. The picture on the left is the principle of feature-level retrieval, and the picture on the right is the principle of scene-level retrieval. A one-way arrow indicates similarity matching between the query scene or corresponding feature and the dataset scene or corresponding feature. Two-way arrows indicate there exists transmission of information between scenes or scenes features, so the scenes in the dataset aren’t feature-level isolated.  
chosen measures like \(L2\). That is to say, they first use different \"distance measures\" to attain various affinity graphs, and then re-rank scenes by calculating the similarities between nodes within the affinity graph. To enhance multiple complementary features efficiency in RSISR, [PERSON] et al. [66] employed the model that crossed diffusion. In this paper, the collaborative metric fusion is the graph-based retrieval algorithm, which shares information in multiple feature spaces in the fusion process. It builds the relationship among multiple types of deep features, and imports other auxiliary images in the dataset while maintaining the discriminating power. Here, each initial graph is constructed by one corresponding feature. The image retrieval task can be finished immediately just by searching the graph's affinity matrix rather than distance measures, and this method can achieve a dataset-level precision of 76.57%, which is 33.26% higher than the framework which uses \(L2\) in [56].