# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 3 Results
### Visualizations  
Visualization of the final results of geocomputation in the form of an image is possible; however, it is critical, due to the automatic clipping of raster, to have huge integer values by most open source and commercial computer GIS applications to different maximal values.  
The result is one map containing all the information from the nine spectral bands. It is reversible, which means that information about any spectral band can be extracted from it. This is possible because of the simple scalar multiplication and summation of stacked pixel values from all spectral bands using the vector (\(256^{0}\), \(256^{1}\), \(256^{2}\),..., \(256^{8}\)). The reverse procedure involves recursive divisions and distribution of appropriate remainders using the above values.  
No GIS software (available at this moment and known to the author) was able to display the full histogram of the image (most clipped the maximal values). Visualization of the resulting image containing information from all the spectral bands was possible using the export to a 24-bit depth geotiff file (presented in Figure 2).  
That is why the original resulting image was rescaled into the range \(<\)0, 255\(>\), rounding off the values to the nearest lower positive integer (again using the raster calculator). The flattened histogram, i.e., the greyscale values, are presented in Figure 3 and spatially visualized in Figure 4.  
The resulting rescaled image can be visualized as a single band, greyscale image (in Figure 4a). This is an artificial procedure that develops a false image, it is however sufficient, at present, in order to illustrate the usefulness of all the aforementioned steps. This visualization involves all the data from the nine bands of the Landsat ETM+ original satellite image. However, due to rounding and rescaling, the original input values of the band components cannot be reconstructed (excerpted from  
\begin{table}
\begin{tabular}{c c} \hline Name of file: LE07\_ABCDEFGHI.grd Size: 939 rows and 1641 columns \\ EPSG = 32634 PROJ\_DESC = UTM Zone 34/WGS84/meters \\ PIXEL WIDTH = 30 m PIXEL HEIGHT = 30 m \\ \hline Grid Data Maximum: & \\
4.43776518668 \(\times\) 10\({}^{21}\) & Grid Data Minimum: 0 & Grid No-Data Value: \\
4,437,765,163,592,664,000,000 & 1.70141 \(\times\) 10\({}^{38}\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the resulting image of the RS algorithm application of the testing scene.  
the transformed image). It is only possible to obtain these data from the original outcome of the RS algorithm application. The transformed image seems to be very sharp; the details are distinguishable, and are comparable to other results of the above-mentioned algorithms and methods of the data fusion. The same image displayed using pseudo-color technique reveals some more details (Figure 4b).  
Figure 3: The flattened histogram of the resulting rescaled image from the RS algorithm application (linear scale, class 0: omitted due to no data value; SW/left bottom corner/part of testing scene).  
Figure 2: Visualization of the resulting image from the RS algorithm application exported as a 24-Bit depth geotiff image (greyscale).