## 4 Remote Sensing Image Scene Retrieval
### Retrieval with the Aid of Hash Learning  
The approaches in the last part use the graph-based model to measure the similarity, but the graph model cannot easily be applied to large-scale RSISR, because it requires a large memory and the computation of it is very complex. In addition to improving feature retrieval skills, there is also a feasible way to reduce the dimensionality of features. In the former method, there are methods based on trees, such as the K-Dimensional (K-D) tree which divides data into subspaces and stores partition patterns through trees. This method does improve the retrieval speed, but can greatly damage the performance, especially when the high dimension of features makes the tree more complex. Obviously, they are not suitable for RSISR. There are alternative solutions (i.e., the methods which reduce the dimension of feature) to overcome the shortcomings of methods based on the tree. For instance, [73] employed the Principal Component Analysis (PCA) to map RS features learned by traditional CNN to Deep Compact Codes (DCC). Recently, hash learning methods have been used to address large-scale RSISR, and have been shown to behave promisingly [74; 75; 76; 77; 78]. The hash functions change features from high vectors into binary low vectors (i.e., the vectors are made of binary hash codes), and the primitive structure can be preserved. Figure 5 shows how the amount of memory required is largely reduced, whereas the retrieval efficiency is enhanced (i.e., the Hamming distance can be calculated efficiently) in the low-dimensional binary feature space (i.e., Hamming space). Compared to the graph model, there is neither high computational complexity nor storage complexity of graphs in hash learning. The constraint among the query image and retrieved images are denser. To overcome the problem of how linear search is time-consuming and unsuitable for multi-scale situations, [75] first introduces two nonlinear kernel-based hashing methods. The first method only uses untagged images, and the hash functions are defined in the kernel space. By contrast, the second method uses the semantic similarity excavated from annotated images to describe the  
Figure 5: Schematic diagram of hash learning. In large-scale remote sensing image scene retrieval (RSISR), hash learning enormously reduces the storage while maintains the discriminating power of features, as well as transferring visual features to hash codes. unique hash function in the kernel space. However, the learning time complexity of these methods can be high and cannot achieve a quick response, so [76] proposes a novel data-independent hashing method, named partial randomness hashing (PRH). Later, [PERSON] [74] advocated an unsupervised feature learning method. The hash learning module probes the hashing function. [PERSON] learned the hybrid feature mapping function on column sampling hashing, where the precision and recalls are even larger than when using the former KSH (kernel-based nonlinear hashing in [75]). The approach has surprisingly better retrieval performance in that the mean average precision (MAP) values exceed those of KSH by about 45%, which can train all of the data that is available. As [PERSON] et al. [79] brought the hashing method to hyperspectral RS scenes, deep features which are spectral-spatial and produced by the Deep Convolutional Generative Adversarial Networks (DCGAN) model were then Nonlinear Manifold [80] (NM) hashed to reduce their dimensionality. Multi-index hashing was also employed to search hyperspectral images. There is a trend where deep feature extracting and hash learning need to be jointly trained in one network, so that the retrieval performance could be enhanced in an end-to-end manner. The similarity metrics that are learned can be more appropriate for the deep features. The deep hashing neural networks [81] (i.e., DHNNs) are developed, while both deep feature learning and hash learning neural networks are constructed and linked.