# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 4 Remarks  
The properties of the above-described procedure makes it possible to segment the resulting image automatically, using only initial (unsupervised) classification of fused image, displaying each level of the greyscale as a different color (pseudo-color technique). Detailed identification of land use classes is the next step and needs use of dedicated (supervised or unsupervised) procedures and algorithms (beyond of the scope and aim of this research). The next step involved only the use of a clustering method--grouping the adjacent (close) values of the image (levels of greyscale, omitting their number, i.e., count)--and it was automatically displayed into the 256 clusters (Figure 3(b)). This clustering procedure was recursively repeated using a manual trial-and-error method of classification that was aimed at adjusting the final classification until a (subjectively) satisfactory result was obtained. The procedure started with the grouping of the ten lowest values, after which water bodies and courses appeared. The reference map was a sheet of an original CLC 2000 map of the same area. No especially dedicated classification procedures were applied; only the grouping of the adjacent values of the  
Figure 4: (a) Visualization of a single band, greyscale image of the transformed and rescaled outcome of the RS algorithm application on an original Landsat ETM+ satellite image, involving information from all nine spectral band components. (b) Visualization of single band, pseudo-color, 256 classes of transformed and rescaled image, outcome of the RS algorithm application, involving information from all nine spectral bands components.  
flattened image and searching for correspondence to the reference CLC generalized map colors as well as the World Topographic (base) map. Visualization of a single band, pseudo color and manually clustered (of adjacent values) image of the transformed and rescaled outcome of the RS algorithm application on the original Landsat ETM+ satellite image, involving information from all nine spectral band components, is presented in Figure 5. The colors are similar to the legend of the CLC 2000 map (Figure 6). The resulting image is displayed as a layer on top of the topographic map.  
Figure 5: Visualization of a single band, pseudo color and manually clustered (of adjacent values) image of the transformed and rescaled outcome of the RS algorithm application on an original Landsat ETM+ satellite image, involving information from all nine spectral band components. The colors are similar to the CLC legend. Displayed as a layer on top of a topographic map.  
Figure 6: CORINE Land Cover map of testing scene [82].  
Simultaneously, the unsupervised k-means procedure for clustering (using SAGA GIS) resulted in another map (Figure 7) using a legend color palette similar to that applied during manual identification of the 24 LU/LC classes referenced to the above-mentioned maps (again checking only that they matched visually).  
The amount of time spent to generate the results of the RS algorithm computation for the whole Landsat ETM+ image (for nine spectral bands, 7451 rows and 8121 columns) is not available due to the overflow memory error. For testing the scene (939 rows x 1641 columns), the application of raster calculator lasted a few minutes. A computer with an Intel(r) Core(tm) i7-2760 QM CPU @ 2.40 GHz processor with 16 GB of RAM was used. Rescaling the resulting image into the greyscale took 1-2 min to test scene. The duration of the initial, unsupervised segmentation of the rescaled resulting image was immediate, but the classification using k-mean clustering for 24 classes lasted over an hour (Figure 7).