## 4 Results and Discussion  
The experiment site was set up by measuring two building walls, a concrete-paved road and a garage sidewall. The general setup of the scene can be observed in Figure 3. The pyranometer data are shown in Figure 1 and constitute an excerpt from a much larger dataset, which was acquired over multiple days. However, for this work, the aforementioned algorithms required only a small portion of the whole dataset. Figure 2 shows the data section that is used in this work.  
Figure 2 shows the relevant section from 15:00 h to 15:45 h with approximate step heating, generated by variation in cloud coverage. The blue curve (GHI) shows the irradiance incident to all horizontal surfaces in the scene, in this case, predominantly the paved road (ROI 3). The orange curve (GVI) was calculated from the GHI data to represent the incident irradiance on all 200\({}^{\circ}\) facing vertical structures, such as the two building sidewalls (ROI 1 and ROI 2) and the garage wall (ROI 4) shown in Figure 3. This explains the difference between the excitation intensities in Table 1.  
Figure 5 was generated using a pixel-wise subtraction of the last image in the heating phase with the first image thereof. Note the uneven heating on the building on the right due to shadows being cast by the roof onto the wall below. This difference image was the basis for both the SHT effusivity estimation and SHC images below.  
Figure 4: Typical thermogram of the scene with the selected ROIs of the experiment. ROI 1 is the left building wall, ROI 2 the right building wall, ROI 3 is the paved road, and ROI 4 the garage sidewall.  
Figure 5: Difference image: last image in the heating phase subtracted by the first image thereof.  
Figure 6 shows the uncorrected thermal effusivity. The shown image assumes a heat flux of 320 W/m\({}^{2}\) for all surfaces, but the real heat flux (albedo correction) must be estimated on a case-by-case basis to obtain correct estimates of thermal effusivity.  
Figure 7 shows the uncorrected heat capacity, neglecting the albedo factor of the objects in the scene. The real surface heat capacity was reduced by a factor of \((1-\alpha)\).  
The LEF algorithm was performed on each pixel of Figure 8 separately. Note that as before, no albedo correction was performed on this image, and each pixel was assumed to have absorbed the full incident irradiation.  
Figure 6: Effusivity image resulting from the SHT approach.  
Figure 7: Surface heat capacity image.  
Note that this effusivity image (see Figure 8) differs from the image generated by the SHT effusivity estimation method (see Figure 6). This is, in part, because the underlying algorithm to determine surface effusivity by LEF was noise sensitive [7], and reducing noise by averaging a whole ROI delivered more reasonable values.  
Table 1 summarizes the results of the different methods for the observed ROIs.  
The garage wall consisted of hollow concrete blocks covered with a layer of gypsum. The two building walls consisted of an unknown type of insulating gypsum covering a conventional building insulation material, such as mineral wool or polystyrene.  
Separating ROIs and correcting the trapezoidal distortion generated by the camera viewing each ROI surface from an angle helped to improve the visual clarity of the image. The results with separation and perspective correction are shown in Figures 9-12. Especially noteworthy are the following distinctive features:  
* Wave-like structure in ROI 1--possible remnant of the insulation installation;
* Hot spot in the top middle of the garage (ROI 4);
* Apparent recognition of the floor in ROI 2.  
Figure 8: Linear effusivity fit image.  
Figure 9: Perspective-corrected difference images.  
Figure 11: Perspective- and albedo-corrected LEF effusivity images.  
Figure 10: Perspective- and albedo-corrected SHT effusivity images.