Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Shelestov, A."'
Autor:
Andrii Kolotii, Hanna Yailymova, Andrii Shelestov, Nataliia Kussul, Vladimir Vasiliev, Bohdan Yailymov, Mykola Lavreniuk, Leonid Shumilo
Publikováno v:
IEEE Transactions on Big Data. 6:572-582
For accurate crop classification, it is necessary to use time-series of high-resolution satellite data to better discriminate among certain crop types. This task brings the following challenges: a large amount of satellite data for download, Big data
Autor:
D. Ju. Yaschuk, A. I. Kosteckiy, B. Ya. Yalimov, Sergii Skakun, Mykola Lavreniuk, A. Ju. Shelestov, S. L. Yanchevskii
Publikováno v:
Cybernetics and Systems Analysis. 52:127-138
Large-scale mapping of land cover is considered in the paper as a problem of automated processing of big geospatial data, which may contain various uncertainties. To solve it, we propose to use three different paradigms, namely, decomposition method,
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XL-7/W3, Pp 45-52 (2015)
One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-tempo
Publikováno v:
IGARSS
For many applied problems in agricultural monitoring and food security it is important to provide reliable crop classification maps in national or global scale. Large amount of satellite data for large scale crop mapping generate a “Big Data” pro
Publikováno v:
Frontiers in Earth Science. 5
Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired
Publikováno v:
International Journal of Digital Earth. 7:829-845
In this paper, we present the service-oriented infrastructure within the Wide Area Grid project that was carried out within the Working Group on Information Systems and Services of the Committee on Earth Observation Satellites. The developed infrastr
Publikováno v:
IGARSS
In the paper we propose the methodology for solving the large scale classification and area estimation problems in the remote sensing domain on the basis of deep learning paradigm. It is based on a hierarchical model that includes self-organizing map
Autor:
A. Yu. Shelestov, S. I. Lavrenyuk
Publikováno v:
Cybernetics and Systems Analysis. 45:881-888
Depending on the problem statement and available information on the system structure and order, three classes of models are discussed: a linear model of state variables with unknown disturbance, a model in input---output variables, and a neural netwo
Autor:
Ladislav Hluchy, A. Yu. Shelestov, A. N. Kravchenko, Nataliia Kussul, V. P. Savorsky, E. A. Lupian
Publikováno v:
Cybernetics and Systems Analysis. 44:616-624
This article presents the architecture of a system that is used for regional water resource quality monitoring of environmental parameters using heterogeneous data sources such as remote sensing data, model data, and data of in-situ observations. The
Publikováno v:
IGARSS
In this paper we discuss advantages and benefits of Sensor Web approach to flood monitoring and risk assessment. A general framework of using Sensor Web based services is discussed that incorporates heterogeneous data sources to provide disaster haza