Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/101563
Title: Preclassification of remote monitoring data in change detection tasks
Authors: Myasnikov, F. S.
Ivanov, O. Yu.
Issue Date: 2020
Publisher: American Institute of Physics Inc.
Citation: Myasnikov F. S. Preclassification of remote monitoring data in change detection tasks / F. S. Myasnikov, O. Yu. Ivanov. — DOI 10.1063/5.0028409 // AIP Conference Proceedings. — 2020. — Vol. 2293. — 140007.
Abstract: Detection of changes occurring on the Earth surface is one of the most important tasks of remote monitoring. It is noted that the traditional methods of detecting changes (basic components, subtraction, division, etc.) are not sufficiently effective when they are applied to the problems of remote sensing of the Earth. This is due to the fact that the images obtained by different systems under different conditions of illumination of the Earth surface have significant differences in their characteristics. To reduce the impact of qualitative differences on the result of processing, it is proposed to use a preliminary classification of objects in the images. The proposed algorithm was tested on the detection of deforestation according to the spacecraft data SPOT and Landsat Recommendations on the choice of parameters of the classification algorithm (frequency channels of images, quantitative and qualitative composition of classes, decision rules, etc.) are formulated. © 2020 American Institute of Physics Inc.. All rights reserved.
URI: http://hdl.handle.net/10995/101563
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85097981380
PURE ID: 20390103
5db3c520-2450-4d78-abfd-7e1dbef846cd
ISSN: 0094243X
ISBN: 9780735440258
DOI: 10.1063/5.0028409
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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