# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 1 Introduction  
Remote sensing is the basic tool for obtaining a huge volume of digital information by sensing reflected electromagnetic radiation in a range of visible radiation, infrared, thermal infrared and microwaves, especially from satellite images. The spectral, spatial and temporal variation of electromagnetic energy coming from the scene contains the desired information [1]. More and more detailed information, inter alia, concerning land use--land cover (LU/LC)--is acquired, and several approaches, algorithms and computer applications have been developed, which are also aimed at the automatic classification of satellite images parallel to the development of both software tools and hardware devices. The main thread of this paper is the data fusion of all possible acquired information, which is used as a case study related to LU/LC identification, but primarily as the tool for data fusion. Further steps and application of further described procedures are left to be decided according the aims designated by researcher. The growing importance of satellite images in environmental monitoring is related primarily to the recent rapid development of the Earth's surface imaging systems [2]. Recent multispectral (MS) satellite images compound many spectral bands and resolution per spectral band that is above 8-bit resolution. Processing these images requires a lot of memory, time, and even a distributed processing approach. Several approaches to multispectral image segmentation allow various classes of LU/LC [3] to be detected, however the application of these methods is not limited to geospatial technologies andgeodisciplines. It is further possible to classify some parts of images by defining the qualitative as well as quantitative characteristics of environmental elements and/or anthropogenic objects, using the combined visible and invisible spectral bands. Formerly used visual interpretation is time-consuming, even for the experienced researcher, and despite strictly defined procedures, it is rarely possible to obtain identical results, even when the action is repeated by the same person [2]. However, finally, a person will always need to evaluate the usefulness and correctness of the results of automatic classification using the set of information and communication tools (ICT) tools developed today. The aim of this research is to verify the possible application of yet another spectral band data fusion approach using the Reed-Solomon algorithm (RS), which is well known in other fields of computer science. It should serve at the automation, preparatory stage of data acquisition, and is not intended to replace the well-defined procedures of the classification of satellite image content. We only aim to automate further steps of the detailed classification easier, e.g., LU/LC identification types. The novelty lies in the different interpretation of the algorithm itself and its scope of application. The empirical case study and the short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible use of the RS algorithm. Moreover, the whole procedure is not in its final form of a complete computer application yet. Some steps are automated using scripting languages, but open source and commercial computer applications have been used at the stages of visualization and interpretation. The rationale behind the application of the RS algorithm for image data fusion includes all possible information from all the acquired spectral bands, assuming that complete composite information in the form of one compound image will improve both the quality of visualization and some aspects of quantitative analyses. Passive optical hyperspectral, multispectral and panchromatic imaging technologies, as well as topographic information, acquired active sensors, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) may be sources of image data fusion, making interpretability easier due to the diversification of information.