# Image Mosaicing Applied on UAVs Survey
## 5 Discussion
They have innovated in introducing this system to daily activities, where the use of these techniques is applied in tasks such as surveillance and monitoring in large areas, but this process can be used for more applications, such as: photogrammetry for archaeology and heritage maintenance, agricultural and forestry surveillance, civil engineering, digital elevation models and 3D mapping, rural roads, geological infrastructure,  
Figure 4: Mosaicing development. Figure 5: UAV mosaicing implementation. Figure 6: Development by continent. road information, urban terrain reconstruction, air analysis and pollution (for environmental awareness), urban configuration, and environmental monitoring, among others, as shown in Figure 8.  
Among the main objectives of this review is to present the most widely applied mosaic techniques of aerial panoramas in drones and, as presented in the previous sections, the feature-based approaches are the most implemented methodology, as shown in Figure 9. More specifically, the approaches based on estimation of the global single transform features, even more than the local hybrid transformation, are more recent methods with some advantages over the earlier methods. However, this may be related to the fact that these methodologies are faster, more documented, and, in some cases, have a low computation cost compared to the local hybrid transformation approaches. Figure 8: Percentage of aerial image applications. Figure 7: Development by country. Figure 6.0 Most used feature methodology. From global single transform feature revision, the most implemented methodologies are the Harris corner, SIFT, FAST, ORB, SURF, and BRISK; these approaches are the most implemented in UAVs. The comparison made between these methods shows that the classic ORB is one of the most applied methodologies due to its speed above other classic methodologies, which makes it a good choice for real-time applications. However, a disadvantage of this method is its low accuracy compared to that of other methods, such as FAST, in which it is based, and BRISK, which outperforms in cases of rotation and fast scale changes. Even so, the SURF technique is implemented more often because of its performance compared to that of SIFT and its speed close to that of ORB; however, if speed is not a key factor but accuracy and robustness are desired, the most applied method is SIFT, as shown in Figure 10, where there are new approaches based on GPU and computing algorithms show an improvement in the speed of SIFT application. In the local hybrid transformation area, the most notorious algorithms are SPHP and APAP, in which SPHP has the advantage by applying local hybrid transformation with similarity transformation to reduce the distortions and preserve the similarity constraints (Figure 11). New approaches based on improvements in the matching and blending methods were considered within the aforementioned categories. As it can be observed from the tables, feature registration is the most implemented in SIFT, SURF, and ORB for recent development. The same applies for SPHP and AANAP methodologies. In Figure 12, a plot of the principal feature-based transform methodologies implemented on UAVs is presented. As it has been presented, since 2017, there has been an increase in this research area, mainly undertaken by universities and research centers, where, it should be pointed out, the main works are carried out in Asia. Nonetheless, many countries have joined this research field, in which new proposals explore the use of machine learning-based systems or artificial intelligence (AI)-based techniques. This does not mean that classic methodologies are outdated, since new approaches propose the combination of feature-based techniques with different algorithms, such as LPM, SR, PCA, and REW, which provide a more robust  
Figure 6.1 Local hybrid transform implemented on UAVs. Figure 6.2 Global single transforms implemented on UAVs. and efficient methodology.