## 5 Results
### Findings of the Multi-Frequency SAR Analysis  
Since the integration of multi-frequency SAR data into the image reconstruction process is the main part of the proposed workflow, the statistical evaluation of the results is presented in detail. The mentioned statistics are calculated for the entire image, not only the cloud masked areas. The results of the mean preservation of the image content by using the MB indicate a spill-over of spectral innovations since almost all reconstructed image bands in all frequency combinations are negatively biased (Figure 13). The weakest performance is achieved by C-Band, which is outperformed by all other combinations by orders of magnitudes. The visible bands, especially the Red band, are most biased, while the infrared bands are in general less biased. The results tend to a loss of image content since the majority of the reconstructed bands have a negative DV (Figure 14). Only NIR and SWIR-1 tend to be subject to the introduction of artificial image content for the most of the frequency combinations. The level of the per-pixel error is denoted by the STD-DI. For the analysis of several frequency combinations, this measure shows no significant differences. The highest deviations from the ideal value are observed for C-Band Intensity and C-Band Intensity \(+\) Texture (Figure 15). The structural similarity of the filled and the reference image, which is expressed by the Correlation Coefficient, shows very high values for all bands in all frequency combinations (Figure 16). Only C-Band Intensity features a slightly weaker performance in terms of structural similarity. The spectral fidelity of the filled images is evaluated by means of SAM. The smallest spectral differences are achieved by the combination of XL-Band Intensity \(+\) Texture as well as XCL-Band Intensity \(+\) Texture (Table 4). The lowest spectral fidelity is achieved with C-Band Intensity and C-Band Intensity \(+\) Texture. Several findings result from these statistical evaluations of the fill quality. In every category the frequency combinations C-Band Intensity and C-Band Intensity \(+\) Texture appear to have the lowest performance. In order to increase the certainty of the performance of the several frequency combinations, the values for each statistical measurement at each band were ranked according to their deviation from the particular optimal value. The result is presented in Table 5. The values rank from 10 (largest deviation) to 1 (smallest deviation). The best mean ranking of all values over all image bands is used to determine the statistically best frequency constellation to restore data gaps due to cloud cover. Based on the results of this ranking, the combination of multiple frequencies is clearly preferable compared with a mono-frequency data basis. Furthermore, the inclusion of the mentioned texture measures increases the performance of the cloud removal algorithm in every case. The findings of the image restoration of the full image by means of multi-frequency SAR data can be outlined as follows (_cf._ Figure 17). 1. The combination of more than one SAR frequency increases the restoration quality. 2. The restoration with inclusion of the derived texture measures outperforms the frequency combinations without texture. 3. Concerning mono-frequency SAR data, L-Band is most suitable for image restoration. For multi-frequency SAR data, the frequency combination XCL features the best statistical budget. 4. Due to the poor data quality of the used ERS image, C-Band data is excluded from any interpretation of the study results. To conclude, Figure 18 shows the reconstruction result for the test area. Figure 14: Difference of Variances (X, C, L = SAR Frequency bands; I = Intensity; T = Texture). Figure 13: Mean Bias (X, C, L = SAR Frequency bands; I = Intensity; T = Texture). Figure 16: Correlation coefficient (X, C, L = SAR Frequency bands; I = Intensity; T = Texture).