# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction
### Image Data Fusion
The most known and used are: Intensity Hue Saturation (IHS), Modified IHS, principal component analysis (PCA), Pansharp, wavelet-based techniques (Wavelet), Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Brovey transform (BT) and Multiplicative [29; 23]. There are well-knownconstraints and advantages to the use of them. All of them transform input images, however only few preserve the spectral fidelity of the input dataset [23]. There are also so-called hybrid approaches; researches combine two or more fusion algorithms. Some of them combine different methods, e.g., General Intensity Hue Saturation (GIHS) with genetic algorithm (GIHS-GA), IHS with the Fast Fourier transform (FFT, called Ehlers fusion) [29; 30]. Some proprietary and patented approaches offer users the adaptation of the fuse methods aimed at spectral integrity, high spatial detail in an intelligent manner depending on the context and target of the image fusion process [29; 31], as well as the use of artificial intelligence (AI)  
Multi-source image data fusion is perceived as the main tool aimed optimization and information extraction from remote-sensing data, and integrates more than three spectral bands [29; 30; 32]. Several software packages (e.g., ENVI, PCI, ERDAS, etc.) included fusion algorithms as available standard routine functions [29]. However, remote-sensing image fusion is possible using different sources, e.g., satellite and airborne, as well as hyperspectral and LiDAR (Light Detection and Ranging) images [29; 33; 34]. The diversity of sources forced new tools (algorithms) to be applied due to new challenges concerning the details of images as well as their parameters, often enumerated as the prioritized sequence of tasks. The prime challenge (mentioned above) concerned the appropriate methods of synthesis of all those multi-source images and/or data sets. The challenge of multiplying spectral bands is also related to changes in the range of the spectral band of new-generation sensors, which in turn affects the quality of the fused image. Other challenges (the threads not touched thereafter) concerned: geometric accuracy, different non-orthogonal angles of acquired observation images, and geometric and radiometric requirements for fused imagery [19; 29]. There are different approaches to data: signal, pixel, feature and decision level fusion. At the signal level, fusion is aimed at creating a new, better quality signal. Compound images create, at the pixel level (fusion created on a pixel-by-pixel basis), an ease to image processing, especially in remote-sensing, medical diagnosis, surveillance and photography applications [35; 36; 37; 38; 39]. At the feature level, fusion highlights some overall parameters like pixel intensities, edges or textures. At the decision level, the fusion multiple methods are applied to elaborate on decision rules and are aimed at leveraging the interpretation of the image, which is usually used in artificial intelligence (AI) techniques [1; 40; 41]. Some researchers also point to the subpixel fusion level, which involves different spatial scales and uses appropriate transformation [41]. Pixel-level image fusion is perceived as the approach that directly combines the original information in the source images, and is aimed at synthesizing a more informative fused image for visual perception as well as computer processing. The main factors leveraging this approach are: (1) the need for combining images from different sensors and camera settings, (2) the development of signal processing and analysis theory, and (3) the growing amount and diversity of acquired images in different fields of applications. Pixel-level image data fusion now a set of tools that can be categorized into four groups: multi-scale decomposition methods (multiresolution analysis), sparse-representation-based methods (component substitution), the methods performing fusion directly to the image pixels or the group of pixels (model-based) and hybrid, mixed methods that combine two or more of the tools mentioned above [1; 39].