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Full-Text Articles in Engineering
The Challenge Of Hyper-Spectral Satellite Imaging And Integer-Valued Fuzzy Sets, Maria Beltran, Vladik Kreinovich, Scott A. Starks
The Challenge Of Hyper-Spectral Satellite Imaging And Integer-Valued Fuzzy Sets, Maria Beltran, Vladik Kreinovich, Scott A. Starks
Departmental Technical Reports (CS)
Satellite images already produce huge amounts of data, which makes their processing a serious computational challenge. This problem will become even more complicated with the launch of multi-spectral Earth-imaging satellites that will increase the amount of information by at least two orders of magnitude. With such a huge amount of information, it is necessary to come up with data processing methods that are as fast as possible. In particular, we show that for fuzzy processing techniques, this leads to the necessity to use integer-valued fuzzy sets.
Environmentally-Oriented Processing Of Multi-Spectral Satellite Images: New Challenges For Bayesian Methods, Scott A. Starks, Vladik Kreinovich
Environmentally-Oriented Processing Of Multi-Spectral Satellite Images: New Challenges For Bayesian Methods, Scott A. Starks, Vladik Kreinovich
Departmental Technical Reports (CS)
Remotely sensed images from new generation satellites present an opportunity for scientists to investigate problems in environmental and earth science which have been previously intractable. The magnitude of data that will arise from these hyperspectral instruments create the need for innovative techniques to accomplish data reduction. This paper presents an algorithm which shows promise as a tool for reducing the dimensionality of data resulting from remote sensing. The optimality criteria for the algorithm is the Bayes Risk in the reduced dimension space.