# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction
### Image Data Fusion  
Data fusion concerns the analysis of several datasets, which can interact with themselves and inform each other [4]. The problem of image data fusion is general and is not only related to remote sensing. The fused image \"retains all the complementary and redundant information of the input images\" [7]. The requirements of the fused image have been defined as retaining complementary and significant information regarding the input images, no presence (or minimization) of artefacts, i.e., appearance of synthetic features not present in real world, and last but not least, should not reduce the quality of view, e.g., noise or fault registration, or the elimination of inconsistencies [1; 7; 8]. The same requirements concern the remote sensing images, as well as the growing significance of the analysis of medical images, using relatively new approaches like, e.g., sparse representation and guided filtering. Similar approaches were aimed at the same targets, but in quite different fields [7; 9; 10]. There are numerous motivations for image data fusion: obtaining systemic global view of fused image, ease of exploratory research aimed improving decision-making, obtaining the answer to specific questions concerning distinctive modal elements (also in time) and extracting knowledge from image data for various purposes. Despite the numerous research papers on the topic, some researchers confirm the opinion that image data fusion is in its preliminary stages [4]. Challenges arise because fusion images concern the visualization of very complex phenomena, which are often dependent on a large number of synergistic features, either biological, environmental, sociological, psychological or urban systems (just to name a few). Such a wide spectrum of phenomena generates, in turn, a diversity of research problems. Visualization during the image fusion process is often based on heterogeneous input images and datasets. [4; 11; 12; 13; 14; 15]  
Earth observation (EO) initiatives play a key role in long-term remote-sensing application domains, such as LU/LC mapping, environmental monitoring, and natural resources, as well as in on-demand situations (e.g., crises), when EO data need to be streamed through time-critical workflows to deliver reliable and effective information [16]. Mapping the complexity of land use/land cover is a challenge, especially in regards to monitoring and managing the environmental and societal impacts of land use. Data fusion providing synergistic information appears to be a promising approach [5]. The challenging and ubiquitous remote-sensing image classifications of land use and land cover are mandatory in multi-temporal studies and are useful input for other research areas. The important challenges to the approach are related to the complex statistical characteristics of images involving high computational problems [17]. The application of classic methods of satellite image content detection of LU/LC is well-known and has a defined procedure using unsupervised or supervised classification or both. There is also the pre-preprocessing step that serves as an integral segment that stands in between data acquisition and analysis steps. Data fusion serves as a cohesive component and routine procedure in rapid information production [18]. It is also a general and recognized research problem involving quantitative and qualitative aspects [18; 19; 20; 21; 22; 23; 24]. The choice of algorithms used in data fusion depends on the scene and purpose of application. A deep insight and review of the possible approaches and algorithms (both open source as well as proprietary) can lead one to find rich literature on the subject. Specialists pointed out that the high spectral resolution is needed in order to classify complex land-use and land-cover types and fusing panchromatic (PAN) and MS images with complementary characteristics can provide a better visualization of the observed area [18; 19; 25; 26; 27; 28]. There are several different fusion algorithms.