## 3 Materials and Methods
### Overview of the Approach
3. description of all detected points by SIFT descriptor,  
4. matching possible tie points (on pair of rasters) in relation to features with the use of the Brute Force Matching algorithm (Triangulation),  
5. adjustment of possible characteristic points by means of the pre-bundle adjustment method on the pair of rasters; the iterative process with point filtration (RANSAC method):  
5.1. filtration under the assumptions of three thresholds 0.5m, 0.1 m and 0.01 m.  
5.2. the analysis of the number and distribution of detected tie points,  
5.3. the analysis of values of deviations on points and distributions of points: If points are evenly distributed within the entire scans and the deviation on points \(<=0.01\) m the detected points are applied during the stage six. Otherwise, the detected points are applied to determine approximate elements of the exterior orientation, considered as the approximation of the ICP method. 5.4. division of tie points into two groups: Control and check points--the method of division of points considered the division of an image into four parts, ensuring equal distribution of both types of points within the entire object. Their number was analysed in each part; if it was greater than six, every sixth point was considered a check point. 5.5. determination of approximate elements of the exterior orientation and the analysis of RMSE values on control and check points used in step six. 6. Final bundle adjustment for all of the pair of TLS data to defined as the reference scan. Figure 2: Diagram of the performed research: Processing and orientation of terrestrial laser scanning data based on the use of raster images in different cartographic projections of the intensity. TLS: Terrestrial Laser Scanning. Algorithms for searching and initial matching of tie points were implemented in the original software application, developed in the C++ programming language. For this purpose, the OpenCV library of functions was used, which allowed for reading raster data, searching for characteristic points using the FAST, SURF, etc., algorithms and describing points by means of descriptors of relative matching of features. The initial filtration of outliers was performed using software tools based on the functions of the Armadillo library [46], utilizing LAPACK algorithms. The analyses of the graphical representations of results were performed using the MATLAB (2016b, MathWorks, Natick, MA, USA, 2016)) application. Data Pre-Processing  
The first stage of raw TLS data processing relies on the import of raw TLS data recorded in the binary form. Thus, it was decided to use a temporary version of the Software Development Kit (SDK) of the Z + F scanner. As a result, it was possible to read TLS data and store it in the ASCII format (a _pts_ file). The number of points was recorded in the Header; the following information was recorded in successive lines: Horizontal and vertical angels, distance, full intensity, and XYZ coordinates of each point. Basing on original (unprocessed) data the full intensity and horizontal and vertical angles (Hz and V) were applied; they were used to generate a spherical image and an orthoimage in the LupoScan application and in the Mercator projection, based on the original application in C++ and ArcMap, applied for generation of raster images. In order to determine the XYZ coordinates of tie points detected in the intensity raster images in the pixel system, it was necessary to perform data conversion in two ways. In the case of the spherical projection and orthoimages, LupoScan software tools were used, and the process itself relied upon import of a text file containing pixel coordinates to LupoScan and export of the obtained XYZ coordinates to a text file. Raster images in XYZ coordinates were determined using ArcGIS software and its Model Builder functionality (the functional diagram is presented in Figure 3).