2018Journal of Optical TechnologyRequires access

Method for reducing the redundancy of optoelectronic remote Earth-probing databased on the restructuring of gray-scale images

А. Н. Григорьев, Е.А. Дудин

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Abstract

This paper proposes a redundancy-reduction (compression) method for the remote Earth-probing data of gray-scale images. This method is based on an image-restructuring prototype and principal component analysis. A technique is developed for analyzing the quality of the redundancy-reduction method for remote-probing data, based on which the method developed here is compared with existing data-compression standards used with remote probing. It is shown that the results of this data-redundancy-reduction method have better quality than those of analogous methods. Particular recommendations are formulated for using this method.

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What this paper is about

This paper proposes a redundancy-reduction (compression) method for the remote Earth-probing data of gray-scale images. This method is based on an image-restructuring prototype and principal component analysis. A technique is developed for analyzing the quality of the redundancy-reduction method for remote-probing data, based on which the method developed here is compared with existing data-compression standards used with remote probing. It is shown that the results of this data-redundancy-reduction method have better quality than those of analogous methods. Particular recommendations are formulated for using this method.

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Available abstract

This paper proposes a redundancy-reduction (compression) method for the remote Earth-probing data of gray-scale images. This method is based on an image-restructuring prototype and principal component analysis. A technique is developed for analyzing the quality of the redundancy-reduction method for remote-probing data, based on which the method developed here is compared with existing data-compression standards used with remote probing. It is shown that the results of this data-redundancy-reduction method have better quality than those of analogous methods. Particular recommendations are formulated for using this method.

Key concepts: Redundancy (engineering), Data redundancy, Computer science, Principal component analysis, Dimensionality reduction, Data reduction, Restructuring, Data mining

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