# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 4 Experiment and Analysis  
This experiment uses four groups of aerial oblique image pairs with POS information as the research object. POS can indirectly provide the camera's angle and line elements. The LJL line matching  
Figure 5: LJL descriptor construction and matching. Two red line segments intersect at red dot as a LJL structure, and two black circles define the sub-regions together with green lines. The blue arrows (8 arrows in one cluster) represent the descriptor in each sub-region. method is used to process each group of image pairs. In order to reduce the time cost, the construction of multi-resolution pyramid is not performed in this experiment. In order to demonstrate the effect of the LJL algorithm visually, and to facilitate comparison with the processing effects of wide-baseline oblique images, firstly, a pair of adjacent conventional short-baseline images are selected on one strip for processing, before performing line matching on oblique images as shown in Figure 6. The matching results are shown in Table 1.  
The second column in Table 1 represents the number of extracted line segments from each of the two images. The third column represents the number of LJL structure descriptors constructed in these stereo images. There are three values in the fourth column. They indicate the number of correctly matched line segments, the total number of matched line segments, and the matching accuracy rate, respectively. The fifth column represents the processing time. It can be seen that the LJL algorithm has a very good effect on the processing of conventional small viewing-angle aerial images, and the accuracy rate can reach to more than 97%, which can be used as a reference for processing the following wide-baseline oblique images. The experimental area shown in the first image pair used in the experiment is located in the northeastern part of China. The distribution of buildings in the survey area is relatively sparse, and the elevation difference is obvious. The second, third, and fourth image pairs are located in the same survey area in eastern China, where the buildings are densely distributed. Most of the buildings are relatively flat; however, certain areas contain obvious elevation differences. The shooting angles of the first image pair are down-looking and back-looking. The second, third, and fourth image pairs are respectively down-looking and back-looking, left-looking and forward-looking, and left-looking and right-looking. In order to better display the overall shapes and orientation of the images (before and after transformation), the images shown in the figures maintain their intrinsic aspect ratio with no rotations. Parameters of the four image pairs are listed in Table 2. The four pairs of original images are shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conventional** & **Extracted Line** & **Line-Juncture-Line** & **Matched Line** & **Time (s)** \\
**Images** & **Segments** & **(LJL) Structures** & **Segments** & **Time (s)** \\ \hline Image pair & 465,499 & 375,366 & 258/266/97.0\% & 4.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Line matching results of a conventional image pair. Figure 6: The line matching effect of the LJL method on a conventional down-looking image pair. (**a**) First image; (**b**) Second image. It can be seen that many buildings appear rectangular in the down-looking images and approximately trapezoidal in the side-looking images. The circular structure in the down-looking image becomes elliptical in the side-looking images. Perspective transformation matrices are constructed using interior and exterior orientation elements of each image, and the above original images are rectified as new images, as shown in Figure 8.