# Image Mosaicing Applied on UAVs Survey
## 5 Discussion  
The generation of aerial panoramas and mosaicing are very active fields with new approaches each year. The growing trend of this research field can be seen in Figure 4, wherein the period from 2017 to 2019 saw a significant increase in this research field compared to 2020 and the first half of 2021, where the number of articles was nearly half as much as that of the previous period. Thus, it can be assumed that the interest in this field could increase due to the approach applied in these techniques as an effort to solve some of the main problems of stitching methods and the development of different implementations for new UAV applications or improvements in their processes. Some of the issues that are addressed are time processing, matching relation, hybrid transformation to avoid the most common errors of parallax and ghosting images, and image composition by applying different methodologies and techniques based on feature registration mosaicing methods. As it can be observed in Figure 5, the implementation of mosaicing methodologies has increased by almost 46% between 2010 and 2015. In that way, the process has changed from applying the stitching technique on aerial images to implementing it on UAVs by focusing on solving the main problems related to image acquisition from a moving camera, where new solutions based on preprocessing filters are used to improve the feature detection, thus ensuring the relation for the stitching process, or by performing gain compensation techniques on the resulting mosaic image. \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON] et al. [104] & A superpixel image is generated, improving the efficiency and flexibility of the target image to reduce the color differences between the two input images. \\ \hline [PERSON], [PERSON] [105] & The SPHP algorithm is improved, removing the ghost image of the stitched image and generating better stitching results. \\ \hline [PERSON]. [PERSON] et al. (2019) [112] & Image stitching improves from the captured video by eliminating the ghosts caused by moving objects and object detection module, providing high detection accuracy. \\ \hline [PERSON] et al. [15] & The SLIC algorithm is used to generate superpixels in the seam cutting and color blending stages, affording spatial coherency and improving the efficiency. \\ \hline [PERSON] et al. [107] & The local alignment model introduces parallax errors as a constraint term into the minimum energy function and uses the mesh-based deformation to accelerate the calculation. \\ \hline [PERSON], [PERSON], [PERSON] et al. [113] & The inaccuracy results are compared with RMS and show an improvement compared to APAP, SPHP, and REW in time processing. \\ \hline [PERSON], [PERSON], [PERSON] et al. [109] & The accuracy of the method is improved, compared to most used mesh analyses, and the computational cost is comparable to that of AANAP. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mesh-based methods applied on UAV. This study found that the main contribution to this area is presented by the Asian continent, locating it as a zone of interest in this research field. For this reason, it is important to remain close to the progress generated in this region, as shown in Figure 6. As it can be noticed, another region whose progress is important to follow is the American continent, which has remained active in this research field. From these regions, the countries that have contributed the most are presented in Figure 7. The first place is China, followed by India, Taiwan, and Korea from Asia. In the case of America, the USA is the country that has contributed the most in this region. This may be due to the development of new applications based on the mosaic of aerial images applied in a wide variety of areas. China has been one of the main countries involved in the growth and development of technologies but also one of the largest producers of drones in the industry.