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Improving the Efficiency of the ERS Data Analysis Techniques by Taking into Account the Neighborhood Descriptors

1
Institute of Electronics and Lighting Engineering, National Research Mordovia State University, Saransk 430005, Russia
2
Geographical Institute Jovan Cvijic, Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
3
South Ural State University (National Research University), Prospekt Lenina, 76, Chelyabinsk 454080, Russia
4
Geography Faculty, National Research Mordovia State University, Saransk 430005, Russia
5
Ural State Forest Engineering University, Sibirsky tract, 37, Ekaterinburg 620100, Russia
*
Author to whom correspondence should be addressed.
Received: 20 April 2018 / Revised: 28 May 2018 / Accepted: 29 May 2018 / Published: 30 May 2018
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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Abstract

Planning based on reliable information about the Earth’s surface is an important approach to minimize economic expenses conditioned by natural factors. Data collected by Earth remote sensing (ERS), as well as the analysis of such data using automated classification methods, are becoming more and more important for research and practice activities related to assessing the spatio-temporal structure and sustainability of the Earth’s surface. The analysis of the authenticity of the surrounding areas enables a more objective classification of land plots on the basis of spatial patterns. Combined use of various environmental descriptors enables high-quality handling of neighborhood properties, as each descriptor provides its own specific information about a geospatial system. Experiments have shown that the diagnostics of the emergent properties of such internal structure by analyzing the diversity of dynamic characteristics allows reducing exposure to noise, obtaining a generalized result, and improving the classification accuracy. View Full-Text
Keywords: Earth remote sensing; automated classification; neighborhood descriptors; Fisher Vector; invariant and dynamic properties Earth remote sensing; automated classification; neighborhood descriptors; Fisher Vector; invariant and dynamic properties
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Yamashkin, S.; Radovanović, M.; Yamashkin, A.; Vuković, D. Improving the Efficiency of the ERS Data Analysis Techniques by Taking into Account the Neighborhood Descriptors. Data 2018, 3, 18.

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