## 3 Integration of Sensors and Geometric Processing
### Georeferencing of 2D Imager Data
#### 3.3.2 Georeferencing of Sequential and Multi-Camera 2D Imagers  
The multispectral and hyperspectral sensors based on multiple cameras or tunable filters produce non-aligned spectral bands. The straightforward approach would be to determine the exterior orientations of each band individually using SfM. If the number of bands is large, for example 20 or 100, the separate orientation of each band can result in a significant computational challenge, and therefore, solutions based on image registration are more feasible [109]. The transformation can be two-dimensional (such as rigid body, Helmert, affine, polynomial, or projective) or three-dimensional, based on the collinearity model and accounting for the object's 3D structure, i.e., the orthorectification [110].  
Figure 4: TerraLuma 2D imager system (**top**) with exemplary device interaction flow chart (**bottom**; source: [PERSON], TerraLuma group).  
[PERSON] et al. [111] presented an approach utilizing the relative calibration information and projective transformations for the Mini-MCA lightweight camera, which is composed of six individual, rigidly assembled cameras. They used a laboratory calibration process to determine the relative orientations of individual cameras with respect to the master camera in the multi-camera system; the relative orientations of the master camera (red band) and an additional RGB camera were determined. The RGB camera was oriented with bundle-block adjustment, and the exterior orientations of the rest of the bands were calculated based on the relative orientations and the exterior orientations of the reference camera. An accuracy of 0.33 pixels was reported in the registered images. Several researchers reported accuracies on the level of approximately two pixels when using approaches based on 2D transformations with the mini-MCA camera [112; 113; 114].  
In the cases of tunable filters such as the Rikola camera, each band has a unique exterior orientation. [PERSON] et al. [67] showed that the geometric challenges increase with the decreasing flight height and increasing flight speed, time difference between the bands, and height differences among the objects. In several studies, good results have been reported when using 2D image transformations in flat environments [44; 115]. If the object has great height differences, such as forest, rugged terrain, and built areas, the 2D image transformations do not give accurate solutions in general cases; however, good results were reported also in a rugged environment when using 2D image transformations if combined with image capture based on stopping while taking each hypercube [68]. Image registration based on physical exterior orientation parameters and orthorectification should be used when operating these tunable filter sensors from mobile platforms in environments where the object of interest has significant height differences. [PERSON] et al. [67] developed a rigorous and efficient approach to calculate co-registered orthophoto mosaics of tunable filter images. The process include the determination of orientations of three to five reference bands using SfM, subsequent matching of the unoriented bands to the reference bands, calculation of their exterior orientations, and the orthorectification of all of the bands. Registration errors of less than a pixel were obtained in forested environments. The authors emphasized the need for proper block design in order to achieve the desired precision.