Extension of training set using mean shift procedure for aerospace images classification

Extension of training set using mean shift procedure for aerospace images classification

An effective method of training set extension for aerospace images classification is proposed. The method is based on mean shift procedure with respect to spatial information. It allows considering the unlabeled data structure. The results of experimental study using the Salinas hyperspectral image...

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Bibliographic Details
Main Authors: Sinyavskiy Yuriy N., Melnikov Pavel V., Pestunov Igor A.
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:E3S Web of Conferences
Online Access:https://doi.org/10.1051/e3sconf/20197501010
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spelling doaj-daaabb9cc6e743a0a2ea4f74108647a82021-03-02T07:33:35ZengEDP SciencesE3S Web of Conferences2267-12422019-01-01750101010.1051/e3sconf/20197501010e3sconf_rpers2018_01010Extension of training set using mean shift procedure for aerospace images classificationSinyavskiy Yuriy N.Melnikov Pavel V.Pestunov Igor A.An effective method of training set extension for aerospace images classification is proposed. The method is based on mean shift procedure with respect to spatial information. It allows considering the unlabeled data structure. The results of experimental study using the Salinas hyperspectral image are presented, proving the effectiveness of the proposed method.https://doi.org/10.1051/e3sconf/20197501010
collection DOAJ
language English
format Article
sources DOAJ
author Sinyavskiy Yuriy N.
Melnikov Pavel V.
Pestunov Igor A.
spellingShingle Sinyavskiy Yuriy N.
Melnikov Pavel V.
Pestunov Igor A.
Extension of training set using mean shift procedure for aerospace images classification
E3S Web of Conferences
author_facet Sinyavskiy Yuriy N.
Melnikov Pavel V.
Pestunov Igor A.
author_sort Sinyavskiy Yuriy N.
title Extension of training set using mean shift procedure for aerospace images classification
title_short Extension of training set using mean shift procedure for aerospace images classification
title_full Extension of training set using mean shift procedure for aerospace images classification
title_fullStr Extension of training set using mean shift procedure for aerospace images classification
title_full_unstemmed Extension of training set using mean shift procedure for aerospace images classification
title_sort extension of training set using mean shift procedure for aerospace images classification
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2019-01-01
description An effective method of training set extension for aerospace images classification is proposed. The method is based on mean shift procedure with respect to spatial information. It allows considering the unlabeled data structure. The results of experimental study using the Salinas hyperspectral image are presented, proving the effectiveness of the proposed method.
url https://doi.org/10.1051/e3sconf/20197501010
work_keys_str_mv AT sinyavskiyyuriyn extensionoftrainingsetusingmeanshiftprocedureforaerospaceimagesclassification
AT melnikovpavelv extensionoftrainingsetusingmeanshiftprocedureforaerospaceimagesclassification
AT pestunovigora extensionoftrainingsetusingmeanshiftprocedureforaerospaceimagesclassification
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