# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 5 Discussion  
All of the operations described above deliberately omitted flexible, complex procedures using sophisticated algorithms of unsupervised or supervised LU/LC classifications. The aim was only to check whether the application of the RS algorithm outcomes was reasonable, and the aim was not complete because precise classification of land use types was not finished. The RS algorithm was applied as a data fusion tool, and further arithmetic operations concerned the digitally represented reflected electromagnetic radiation. However, the resulting (synthetic) image represented the nominal scaling, i.e., each small or huge integer value of any pixel in the resulting image corresponded to a unique combination of compound values of the set of certain spectral bands. This is a mutually unequivocal function. Any further transformation of the resulting image may destroy this function. This means that grouping (e.g., coloring) and/or clustering the pixels while retaining the original input values of pixels is safe. Further, the RS algorithm created data fusion that layered (stacking) the sequence of input values as groups differentiated by an order of magnitude of numbers. The initial automatic classification of resulting fused image was the final step of data fusion procedure using the RS algorithm. It seems that the main thread and aim of the research had been obtained. However, there are further steps related to precise identification of land use types, which use well-known generalization and supervised or unsupervised methods of classification. The idea of using a similar approach, in the form of another use of RS algorithm, was signalized in former studies [75]. The only condition was the accurate and detailed development of the image data fusion method using  
Figure 7: Visualization of the resulting transformation image using k-mean clustering of 24 LU/LC classes. The colors are similar to the CLC legend. Displayed as a layer on top of a topographic map. the RS algorithm prior to that. These next steps of classification appeared as another thread and the subject of separate studies (beyond the scope and aims of this paper). Sample application of the data fusion resulting image concerning LU/LC classification is only one thread of the RS algorithm application, but shows the possible simple ways of visualization and analysis in further steps. The challenge is to find a way to display the outcome, synthetic image as well as its histogram. No GIS software tools available (available and known to the author) can do this without additional operations, which unfortunately destroy this precision and make it impossible to recalculate (reconstruct or extract) the original spectral band inputs. Another aspect is related to statistical analysis of the resulting image. Due to possible treatment of the resulting image as a set of qualitative features (represented by the sequences of integers), some non-parametric approaches may be applied in further steps. These would be preferably median or mode, and quantiles, because these parameters can be reconstructed (recalculated) and the input combination of the values of given spectral bands can be shown. An example of a reverse procedure for obtaining the original input of spectral bands from the (random) pixel value of the resulting image is presented in Table 2. These are recursive steps of modulo division using parameter \(\alpha\) for a sequence of obtained quotients. The RS algorithm may be theoretically applied to any positive integers, i.e., there may be any number of spectral bands and any number of levels of registered electromagnetic radiation in the above-described problem of LU/LC classification. The RS algorithm can evaluate them, but the only limits are related to hardware and software resources. The only requirement is constant input parameters \(\alpha\) and \(k\). The hidden assumption is a strictly defined ascending sequence of calculation, which must be reproduced in the reverse procedure (Algorithm 2).