## 6 Discussion
### Is Cloud Removal Feasible with SAR Data?  
The major hypothesis within this study is the similarity of physically based relations between the reflectance in the VIS/NIR part and the microwave part of the EM spectrum. As already demonstrated by means of Figure 1, VIS/NIR reflectance and SAR backscatter are closely related. Nevertheless, the information is not completely similar. One drawback of the proposed method is the fact that multi-frequency information is needed to represent the information content of the multi-spectral imagery via SAR data. Further investigations on the similarity of single as well as multiple frequency bands with multi-spectral imagery are necessary to draw final conclusions. The performance of single frequency SAR bands for cloud removal is highly variable. Justified by the data quality, the results of the C-Band data should be excluded from any interpretation in this specific study. For the frequencies X- and L-Band it is not possible to draw uniform conclusions. The statistical quality is very variable regarding the different Landsat image bands. The rankings (_cf._ Table 5) indicate that L-Band performs better than X-Band in terms of the statistical evaluation protocol. The visual comparison depicts a better capability of edge preservation in agricultural areas at X-Band, but a better restoration of DNs in forests at L-Band. Hence, it is recommended to carry out a more detailed quality evaluation with various land cover types before drawing final conclusions.  
SAR speckle may be a source of clutter in the restored images. The influence of speckle is reduced significantly due to the partially very strong multi-looking of the data. Nevertheless, the influence of speckle is still not negligible. This clearly indicates that filtering is a suitable approach to increase the image reconstruction quality. In this study, spatial averaging was carried out by simple median filtering using a 3 \(\times\) 3 pixel kernel size. A further examination of averaging techniques was beyond the focus of this study. According to the study results, a major improvement of the image restoration results can be expected by a suitable definition of spatial averaging algorithms. For this reason, the image fusion should be carried out on a higher level. Feature level fusion (_cf._[45]) prepends the extraction of non-overlapping adjacent homogeneous image objects before the virtual image restoration. The extraction of image objects enables the reduction of the influence of speckle [46]. Furthermore, an object oriented approach would allow for the compensation of spatial scale dependencies due to divergent spatial resolutions of the input data.  
Multi-frequency SAR data outperform single frequency fill images in terms of statistical quality. This is indicating that the introduction of several wavelengths is stabilizing the capability to relate SAR backscatter intensity to optical/infrared reflectance. On the other hand, it has to be kept in mind that an increase of frequency bands, and therefore texture layers, is supposed to increase the quality by definition. As the number of layers contributing to the derivation of the Closest Feature Vector of a certain pixel increases, the agreement is more reliable. Therefore, it is in dispute if the increasing statistical quality is based on the inclusion of multiple SAR frequencies or simply the result of the inclusion of a higher number of image layers.