# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 2 Bibliometric Study and Typical Aerial Camera-to-Ground Target Localization Algorithm  
### Bibliometric Study Based on Web of Science Database  
Firstly, this paper provides statistics and analysis on the documents and patents included in the web of science that are related to target positioning. Since 2005, with the development of camera imaging technology, UAV/MAV loading platform technology, and image processing technology, papers with remote sensing images and target position as keywords show significant growth. The number of relevant examples of literature published each year is shown in the Figure 1.  
It can be found that urban surveying and mapping, geographic survey, resource detection, environmental monitoring, and other remote sensing-related work are the main application directions of the target positioning algorithm through the analysis of the research contents of the literature. This is because the geographical location of the image is closely related to these works, and the load for acquiring the image is mainly UAV, aerial camera, photoelectric pod, etc. In the statistical literature, the main research directions include engineering (1285), computer science (1111), Instruments Instrumentation (869), and Geochemistry Geophysics (519).  
Figure 1 illustrates that the growth rate of the number of papers in the past five years is significantly higher than that before 2016. This is due to the development of target detection of deep learning; more and more scholars have introduced it into the image processing process of small UAVs and other devices. Although this kind of research involves the keyword of target location, most of the target detection algorithms can only give the location of the target in the image and do not calculate its geographic location, which is not within the scope of this study. When the single target detection algorithm and the positioning algorithm with control point are eliminated, the distribution obtained by analyzing the article according to the difference of the basic model of the positioning algorithm is shown in Figure 2.  
Obviously, the target location algorithm based on a single image and the earth model is still the mainstream of current research and the basis of other algorithms, which also has relatively extensive research. The research on algorithms similar to the building target location, forest featureless point location, and ocean ship location are also mostly analyzed for a single image. Further, the location algorithm based on multi-point measurement, multiple shooting, or video tracking has been a research hotspot in previous years, which can effec  
Figure 1: Number of papers and patents published each year.  
Figure 2: Distribution of positioning foundation model.  
tively reduce the impact of random error factors. In the last five years, research regarding the hybrid positioning algorithm has been relatively extensive. In addition to the hybrid computation of various earth models, elevation models, and imaging models, the artificial intelligence algorithms related to image processing (target detection, image super division, image registration, etc.) are gradually integrated into the target positioning technology.