## 4 Remote Sensing Image Scene Retrieval
### Retrieval by Distance Measures
The Low-Dimensional Convolutional Neural Network model (LDCNN), which is pre-trained, is used to automatically extract deep features from the scenes and predict the labels of them [59]. They adopted \(L1\), \(L2\), and the Correlation and Cosine similarity as distance measures. Just as [PERSON] [60] utilizes CNN networks later, for both LandUse and SceneSat datasets, the descriptors based on CNN are more excellent than other descriptors in RSISR of any class. The retrieval performance of Cosine is near to that of \(L2\). Meanwhile, the Average Normalized Modified Retrieval Rank (ANMRR) of \(\chi^{2}\) outperforms that of \(L2\) by 0.163 using SatResNet-50, which is slightly smaller (0.045) than that of \(L1\) in his work. Deep metric learning CNN was introduced to RSISR by constructing a Triplet network with a metric learning objective function, which extracts the representative deep features in a semantic space. In such a semantic space, simple distance measures such as \(L2\) can be used directly. A method based on the FC layer of the CNN (supervised learning) reduces the dimensionality of the semantic features, which further guarantees that the similarity matching is efficient and that the storage is not too large. Instead of simply taking a distance as the similarity, as many content-based RSISR ways do, a retrieval method [61] based on weighted distance and fundamental features of fine-tuned CNN is demonstrated. The CNN model is used to weigh the weight of image classes one by one, and employ them to calculate the \(L2\) between the image which is queried and the images in the data set. As one can see, it is simple but efficient. For multi-scale information problems, [62] combined fine-tuned CNN features excavated from multi-scale images or from multiple sub-patches. They utilized concatenation from various scales, and multi-patch pooling based on the retrained GoogLeNet CNN, and were able to obtain higher levels of accuracy in that the Average Normalized Modified Retrieval Rank (ANMRR) value was approximately 3.1% less than that attained by the manual Relevance Feedback VGG Medium (RF VGG-M) descriptor. The similarity metrics are \(L2\), Cosine, Manhattan, and Chi-square. To make the scene sampling more reasonable, [PERSON] et al. [53] used two schemes. Then, they learned latent features using the deep convolutional auto-encoder (DCAE). To find a proper metric for their deep Bag-Of-Words features, they selected eight common distance metrics: \(L1\), histogram intersection, Bhattacharyya, chi-square, Cosine, correlation, \(L2\), and inner products. A relatively good retrieval performance could be attained using \(L1\) and \(L2\) among these metrics. The low-dimensional global descriptors in some of the methods above mean that irrelevant background information may still be encoded. Then, the local convolutional descriptors extracted in an end-to-end manner and based on feature saliency were combined with Vector of Locally Aggregated Descriptors (VLAD) aggregation to achieve compact and descriptive image representations [63]. This effectively enabled fast database searching and made it able to capture deep semantic multi-scale information. The similarity was determined by \(L2\) of the query and database vectors. For multi-label problems, conventional RSISR systems usually perform single-label retrieval, which underestimates the complicacy of RS images, where an image may be labeled by several tags, thus leading to worse retrieval performance. [PERSON] et al. [64] therefore proposed a multi-label RSISR approach with FC networks, and excavated region convolutional features according to a segmentation map of each image. The region features were processed further to gain a feature vector for similarity matching using \(L2\), thus improving the search efficiency and retrieval performance, as well as solving the multi-label problem at the same time. For multi-band information problems, traditional red, green and blue (RGB)-based image representation has been widely used for RSISR.