## 4 Remote Sensing Image Scene Retrieval
### Retrieval with the Aid of Hash Learning
The time spent on the feature extraction can be reduced, and the final features generated from the Deep Hashing Neural Networks (DHNNs) in the large-scale RS image dataset can be calculated before and stored as the data set of features without leading to huge costs of memory. However, a high retrieval accuracy can only be achieved if hash codes are long enough and training images annotated are adequate in this method. To solve these problems, this paper [77] advocates a solution that learns a metric space based on semantics, while only using a few annotated training images for fast and accurate RSISR in large archives. Also in the paper [78], the deep semantic hashing (DSH) model was also end-to-end, but only exploited a few tagged images. Meanwhile, DSH abates the accuracy of the hash codes in that there is no relaxation in its learning. The author experimented on a Canadian Institute for Advanced Research (CIFAR)-10 data set and utilized his DSH, as well as a few hashing methods such as Hashing with Mutual Information (MIHASH), which is famous in the computer vision community. Mean Average Precision (MAP), which was chosen to be the accuracy, showed that the DSH was about 12.7% more efficient than MIHASH when the scenes were 32-bits. Dealing with cross-source (CS) RSISR is also in need. Usually, the scenes retrieved are from the same RS data source. If the same method is applied to retrieve scenes in another data source, then the data shift problem emerges, and the performance decreases drastically. The CS messages can be fused to achieve a higher retrieval performance in the end, and the CS tasks firstly need to be dealt with [82]. For instance, the MAP values of the proposed approach overwhelm those of other famous methods like DCMH for about 0.15 in the computer vision community when they are applied to solve RSISR CS tasks. The CS deep hashing is even more necessary because the volume of CS remote sensing data is continually increasing. Meanwhile, RSISR results vary among datasets, since the CNN models learned are different. Only source-invariant deep hashing methods, such as source-invariant deep hashing convolutional neural networks (SIDHCNNs) can overcome these two shortcomings. In this example, CS messages in multi-spectral images (high spectral resolution) and panchromatic images (high spatial resolution) are fused to achieve a higher retrieval performance in the end. From distance measures to graph models and hash learning, existing methods can mine complementary messages of multiple features and multiple scales more effectively [74]. The retrieval performance is becoming better (generally, the precision is enhanced from less than 60% to more than 70%). To conclude, the deep feature obtains cracking behaviors in RSISR [53], and the expensive learning process needs to be more unsupervised. Hence, creating more effective and cheaper deep feature extraction methods are of great necessity. Although these approaches could largely accelerate searching speeds, the accuracy levels of retrieval cannot practically apply to applications. On average, the cost of an operation is quite small. By contrast, the individual cost of an operation might be quite high. Moreover, the modeling and learning of deep hashing neural networks (DHNNs) based on specific RSISR tasks deserve more exploration. In the literature, CS RSISR is rarely discussed [82], but CS RS data is increasing ceaselessly, thus stimulating us to further our work on CS RSISR.