# Image Mosaicing Applied on UAVs Survey
## 2 Panorama Generation  
The basis for image stitching is to relate two images using a geometry model that associates the motion from one image with another; the motion that best fits this relation is the projective transformation, also called the homography matrix [36], which gives an aligned eight-parameter model preserving the straight lines [37; 38]. For feature-based methods, the most acknowledged approaches are global single transformation and local hybrid transformation [39]. The sequence followed by these techniques, shown in Figure 1, generates a mosaic. The first stage is image acquisition. This can be achieved by using one camera for translational or rotational acquisition, as shown in Figure 2. This task can be performed in different ways: by using a moving camera, by using more than one camera [40; 41; 42] fixed on a frame to acquire multiple images at once from different angles, or by using a video camera sequence [43]. To perform the relations between the images, it is important to obtain \begin{table}
\begin{tabular}{l l l} \hline \hline
**Characteristics** & **UAVs** & **Satellites** \\ \hline Flexibility & High & Low \\ \hline Cloud dependence & No & Yes \\ \hline Direct meteorological constraint & Wind and precipitation & No \\ \hline Operator required & Yes & No \\ \hline Payload & Interchangeable & Permanent \\ \hline Legislation & Restrictive & None \\ \hline Data update & Constant Refreshing & Periodical \\ \hline Working Time & Short (battery life) & Long (Limited to satellite life) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: UAV and satellite characteristics comparison. Figure 1: Mosaicing process. the camera parameters, such as focal length, that are used in the perspective and projection algorithms [44; 45]. The second stage is feature registration, where different features are detected and matched. These features can be: points, lines, or their combinations in general [46]. The third stage is transformation estimation. Once features are established, a register of both images is created from the features detected. Some cases lead to a mismatch between the key points. For this, different algorithms are used to search for the features with the closest distances between the images, as KD-tree, k-nearest neighbor (KNN) pattern classification, and Hamming distance [2; 47; 48; 49] search for the closest distance from the query location. \[\hat{x}^{\prime}=H\hat{x} \tag{1}\]  
where \(\hat{x}^{\prime}\) is \(x\) in homogeneous coordinates and \(H\in R^{3\times 3}\) defines the homography [49]. Different techniques are proposed to calculate the homography. In practice, robust statistical techniques are employed on a large number of matching points or lines after normalizing the data; these techniques reduce the adverse effects of noise by using the sum of squared difference method or an iterative mathematical model, such as RANSAC (random sample consensus) [50], PROSAC (progressive sample consensus) [51], or direct linear transformation (DLT), to relate the features and reduce the matching points. For feature-based methods, the most used techniques are DLT and RANSAC for their performance and robustness [52]. RANSAC uses the smallest data set possible and proceeds to enlarge this set with consistent data points [53]. The goal is to determine a set of inliers from the presented correspondences so that the homography can be estimated optimally from these inliers [52]. The fourth stage is the warping or stitching phase, where the images are overlapped to stitch together as one. After the matching, the not overlapped region has reprojection errors. In order to solve this problem, the algorithm of bundle adjustment is used. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal 3D structure and viewing parameter (camera pose and/or calibration) estimates.