# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 3 Experimental Results and Discussion
### Multi-Modal  
Considering the increasing popularity of sensors and cameras, the multi-modal matching of images is a growing demand in many applications. We selected some of the most common examples: the combination of thermal and visible imagery for a building (Figure 6a); scanning electron microscopy (SEM) images, including a backscattered electrons and secondary electron images of a mineral surface (Figure 6b); the combination of visible and range imagery from a  
Figure 5: Multi-view datasets: facade (**a**), forest (**b**), playground (**c**), boulder (**d**), graffiti with small geometric differences (**e**) and graffiti with large geometric differences (**f**).  
laser scanner (Figure 6c); the combination of visible and thermal for aerial imagery (Figure 6d); satellite imagery with different wavelengths, where two images were synthetically derived by applying geometric and radiometric distortions to the other two images (Figure 6e); Magnetic Resonance Imaging (MRI) with different visualization parameters, used to highlight different tissues and synthetically derived images (Figure 6f).  
Figure 6: Multi-modal datasets: visible thermal imagery for a building (**a**), SEM images of a bone (**b**), visible range imagery (**c**), visible thermal aerial imagery (**d**), satellite imagery with different wavelengths and synthetically derived images (**e**), magnetic resonance images with different visualization parameters and synthetically derived images (**f**).  
For the first case (Figure 6a), the matching was carried out for a visible and a thermal image of a building for its energetic inspection. The second case (Figure 6b) is composed of two SEM images: an imaging technique used to analyse the surface of a sample at very high magnifications, used in various scientific fields such as materials science, geology, archaeology and biology to gain insight into the structure and composition of a sample. The third case (Figure 6c) corresponds to a visible and a range image of a heritage building for its 3D reconstruction and texture mapping. The resulting matching can be used to improve the registration between the camera (visible) and the laser (range), and then to map the high-resolution texture coming from the visible imagery into the 3D point cloud coming from the laser scanner. The fourth case (Figure 6d) combines visible and thermal aerial images of a city area using a drone; this type of aerial image can be used for the estimation of land surface temperature or for the study and mitigation of urban heat islands, among other applications. The fifth case (Figure 6e) is composed of two satellite images taken with different sensors and the other two are synthetically derived from the original ones; in this case, the registration is important for automatic georeferencing. The sixth case (Figure 6f) is also composed of four images: two of them are medical resonance images taken using different parameters and the other two are synthetically derived images. Synthetic images represent possible processing and acquisition modifications by the application of geometric (rotation, scale, perspective) and radiometric (brightness, hue and addition of random noise) changes.  
The first (Figure 6a) and third (Figure 6c) multi-modal image pairs contain non-coplanar points; therefore, the evaluation of the matches needs to be carried out using the fundamental matrix. For the rest of the multimodal datasets, the points in the images could be considered totally coplanar, with homography being the best method for their assessment.