Abstract
The Reed-Solomon algorithm is well known in different fields of computer science. The novelty of this study lies in the different interpretation of the algorithm itself and its scope of application for remote sensing, especially at the preparatory stage, i.e., data fusion. A short review of the attempts to use different data fusion approaches in geospatial technologies explains the possible usage of the algorithm. The rationale behind its application for data fusion is to include all possible information from all 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 further quantitative and qualitative analyses. The concept arose from an empirical, heuristic combination of geographic information systems (GIS), map algebra, and two-dimensional cellular automata. The challenges are related to handling big quantitative data sets and the awareness that these numbers are in fact descriptors of a real-world multidimensional view. An empirical case study makes it easier to understand the operationalization of the Reed-Solomon algorithm for land use studies.
Highlights
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 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
The results of RS algorithm geocomputation in the form of a report are presented in Software Surfer mathematical function)
Summary
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. 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 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. 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].
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