# Image Mosaicing Applied on UAVs Survey
## 4 Aerial Panorama Applications
### Feature-Based: Global Single Transformation
#### 4.1.2 Sift  
SIFT is one of the most used algorithms for a scaled invariant detector. Although it is efficient at detecting matching points, the time needed to compute operations is its disadvantage. To improve the processing time, different approaches are proposed, such as the one presented in [81]. They propose a binary local image based on SIFT, reducing the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Author** & **Advantage** \\ \hline X. [PERSON] et al. (2019) [78] & This algorithm improves the stitching speed. \\ \hline C. [PERSON] et al. (2017) [80] & Image matching accuracy is improved with less processing time. \\ \hline Y. [PERSON] et al. (2013) [79] & Efficiency and accuracy are improved by registration constraint. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Harris Corner Applied on UAVs.  
complex operations and speeding up almost 50% faster than the original algorithm. An improved SIFT method called AH-SIFT is proposed in [29]. In this method, the descriptor performs more efficiently than the original SIFT, undergoing various levels of geometric and photometric transformations. An optimized projective transformation method to improve SIFT thermal infrared images is proposed using M-least squares to join images obtained by uncooled thermal infrared video in [82]. An approach presented in [83] enhances the speed estimation of a drone and adjusts the velocity of image acquisition, reducing the ghosting effects. A global motion model is used to predict the overlapped region based on the world coordinate frame. Then, SIFT stitching is applied and image quality is evaluated based on gray relational analysis, improving the accuracy [84]. Using a graphics processing unit (GPU), an implementation called the CUDA-SIFT (Compute Unified Device Architecture) approach [85] achieves real-time mosaic generation and tracking. Another approach presented by [82] is a SIFT stitching process based on random M-least square algorithm and super-resolution processes. Some applications use the SIFT process for an earthquake rescue system, where the image mosaicing is used for an image earthquake damage degree (EDD) analysis. This is performed by evaluating the gray level co-occurrence matrix (GLCM) features along with coarseness, contrast, metric, and filters to analyze the EDD [86]. Following the disaster evaluation developed in [87], a methodology to evaluate open-source systems for Urban Search and Rescue (USaR) is used to determine the location of possible trapped victims for fast 3D modeling of fully or partially collapsed buildings using images from UAVs. In the inspection area, some applications use the SIFT mosaicing approach for inspecting photovoltaic systems (PVs) using UAVs with thermal cameras to record videos using GPS for the trajectory. These images will be used to generate a high-resolution image of a PV zone by using SIFT [88]. A measurable aerial panorama based on panoramic images and multiview oblique images is proposed in [89], and it is divided into major stages: projection, matching, and back projection. The stitching process applied is the SIFT methodology to stitch the projected aerial panorama with a down-looking oblique image and the aerial panoramic image after matching the images by their proposed method. Table 3 shows some of the most recent implementations of the SIFT algorithm in UAVs.