# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Local Hybrid Transformation  
From the feature-based methodologies, the most accurate, mesh analyses, are based on SIFT and SURF features. Some methods focus on image compositing. Once the UAV obtains the aerial image, mesh-based stitching blending methods are applied to improve the panoramic result, as presented in [104], where they propose color blending based on superpixels, using simple linear iterative clusters after generating the SPHP panoramic image. Since the number of superpixels is much less than that of individual pixels, such improvement reduces the computational complexity and processing time compared to multiband blending, color transfer based on image gradients, and color matching blending. Another approach is suggested in [105] by calculating SURF and Harris Corner features to obtain the global homography. Applying PROSAC and KNN, it fuses with MDLT to improve the SPHP algorithm, reducing the ghosting on the overlapped image result. An improvement on AANAP is proposed by using superpixel methodology to improve the compositing image. After relating both images, AANAP improves the alignment accuracy and reduces the perspective distortion [15]; then, seam cutting is applied with superpixel segmentation to reduce the ghosting images, and image color blending is finally applied. To reduce the distortion generated by the global homography, the algorithms AANAP and SPHP use the similarity transform. However, in urban scenes, these algorithms cannot preserve the building lines. New developments propose mixing features' inertial navigation systems (INS) in order to improve efficiency or time processing. An indoor application for SIFT and INS is proposed by [106] for camera pose estimation, improving stitch drone-captured indoor video frames. Pose estimation can be achieved by INS to calculate the relation between image frames captured by the UAV to select the most related and reduce the number of image stitching processes. Another option is the use of SIFT to estimate the global transformation parameter. The result will accumulate registration errors and disregard multiple constraints between images [107] to improve the stitching performance. A shape-preserving transform is used to preserve the geometric similarity before reprojecting, which attempts to retain the shapes of local regions and use multiband fusion to process the gain compensation and obtain a natural-looking panoramic image. A matching improvement is proposed in [108] by using the grid-based motion statistics (GMS) algorithm as a means of encapsulating motion smoothness as a statistical likelihood of having a certain number of feature matches between a region pair and removing the mismatches for applying them: the RANSAC. A region-based methodology uses SIFT as the first step to obtain the global transformation, where the overlapped region is divided into small regions and multiple regions have different weights depending on the local homography [109]. Then, RANSAC is used to reduce the outliers, compared to SIFT, APAP, and AANAP. After the global projection estimation, the thin-plate spline (TPS) with a simple radial basis function type formulates the image deformation, due to its good performance \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Author** & **Advantage** \\ \hline [PERSON], [PERSON] [103] & The positional accuracy of the UAV orthoimage by applying the proposed image registration scheme improves the correctness of the process. \\ \hline [PERSON], [PERSON] [15] & The stitching speed of 100 thermal images within 30 s and RGB correlation and classification are improved. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: BRISK applied on UAVs. in both alignment quality and efficiency, by using REW (robust elastic warping) [110]. REW is a methodology proposed by what can be regarded as a combination of the mesh-based model and the direct deformation strategy to remove mismatches.