Large-Scale Classification of Land Cover Using Retrospective Satellite Data

被引：26

作者：

Lavreniuk M.S. ^{[1
]}

Skakun S.V. ^{[2
]}

Shelestov A.J. ^{[3
]}

Yailymov B.Y. ^{[1
]}

Yanchevskii S.L. ^{[4
]}

Yaschuk D.J. ^{[1
]}

Kosteckiy A.Ì. ^{[1
]}

机构：

[1] Institute of Space Research, National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kyiv

[2] “Integration Plus” Ltd., Kyiv

[3] National University of Life and Environmental Sciences of Ukraine, Kyiv

[4] National Center of Control and Testing of Spacecraft, Space Agency of Ukraine, Kyiv

来源：

Cybernetics and Systems Analysis | 2016年 / 52卷 / 1期

关键词：

data fusion; geospatial data; land cover classification; neural network; satellite data; training and test samples;

D O I：

10.1007/s10559-016-9807-4

中图分类号：

学科分类号：

摘要：

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, the method of active learning from the scope of intelligent computations, and method of satellite images reconstruction. Such an approach allows us to minimize the participation of experts in solving the problem. Within solving the problem of land cover classification we also investigated three different approaches of data fusion. The most efficient data fusion method is one that could be reduced to the problem of classification on the base of time-series images. Developed automated methodology was applied to land cover mapping and classification for the whole territory of Ukraine for 1990, 2000, and 2010 with a 30-meter spatial resolution. © 2016, Springer Science+Business Media New York.

引用

页码：127 / 138

页数：11

共 24 条

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