# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction
### Image Data Fusion
There are numerous, mature approaches of multi-scale decomposition methods: pyramid and wavelet transformation [39; 42], discrete wavelet transformation [39; 43], stationary wavelet decomposition [44; 45]. Other newer methods in this group are dual-tree complex wavelet [46], curve-let [47], contour-let [48; 49], and shear-let [50; 51]. The overwhelming majority of these methods use a dedicated filter applying either mathematical (e.g., directional or bilateral filters) or statistical kernels (e.g., weighted least squares filters). The main challenge concerns the lower quality of the resulting fused image (less satisfactory), as well as the decreasing computer efficiency [39]. Sparse representation-based methods simulate a mechanism of human seeing, assuming that only a few characteristics represent the acquired original images enough to be described. The outline of the method involves the creation of a dedicated coded procedure as well as a dedicated dictionary. The outcome is fused sparse coefficients, which, together with the dictionary, reveal the fused image [52; 53]. There is also a group of methods that operate directly on pixels. The outcome pixel in the fused image may be the weighted average of the corresponding pixels of the input images. The weights may be evaluated using different algorithms: supported vector machine (SVM) or neural networks using also appropriate image segmentation, e.g., edge preserving or majority filters [54; 55]. Other methods use PCA and are aimed at evaluating weights. However, these approaches are assessed as quite limited in relation to data fusion performance. Additionally, there are other procedures used, based on color space as well as dimension reduction [39]. Intensity, hue and saturation (IHS) transform are extensively used [56; 57] for the panchromatic sharpening (pansharpening) problem. This method uses mathematical transformation that converts Red, Green, and Blue (RGB) values into a model of IHS, which represents human color perception, but may produce color distortions in the fused image [39]. There is also a patented method for enhancing the spatial resolution of multispectral imagery using pansharpening (Gram-Schmidt transformation [58]). The last category of approaches uses combinations of two or more of the above-mentioned methods. The rationale behind such a procedure is based on the intention to link special advantages of certain methods. A detailed description of the advantages and shortcomings of such transformations is beyond the main thread of this paper and is presented elsewhere in numerous papers [59; 60; 61; 62; 63; 64]  
All of the above-mentioned methods touch the main problem of decreasing visual distortions when fusing multi-spectral, hyperspectral, and panchromatic images at the pixel level. Pixel-level image data fusion is directed at one of the most important techniques to integrate information from multiple sources, allowing for a comprehensive analysis of the captured scene [39]. The approach described below is related closely to (Geospatial) Object Based Image Analysis (OBIA or GEOBIA), which is directed as the paradigm of new methodology to classify or map VHR (Very High Resolution) images into meaningful objects, i.e., the grouping of relatively local homogenous pixels. The steps of the procedure involve a sequence of image segmentation, object classification and final detection results. The input image is segmented into homogenous regions (objects) that represent a group of pixels, and then the classification procedure is applied. These functions are implemented into almost every geographical information system (GIS) and has been widely used for land cover and land use mapping and tracing changes [65; 66; 67; 68; 69]. Next, there are different methods using several algorithms that can be applied, which are aimed at classifying objects as detection results.