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Spectral-Spatial Methods for Hyperspectral Image Classification. Review. / Borzov, S. M.; Potaturkin, O. I.

в: Optoelectronics, Instrumentation and Data Processing, Том 54, № 6, 01.11.2018, стр. 582-599.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Borzov, SM & Potaturkin, OI 2018, 'Spectral-Spatial Methods for Hyperspectral Image Classification. Review', Optoelectronics, Instrumentation and Data Processing, Том. 54, № 6, стр. 582-599. https://doi.org/10.3103/S8756699018060079

APA

Borzov, S. M., & Potaturkin, O. I. (2018). Spectral-Spatial Methods for Hyperspectral Image Classification. Review. Optoelectronics, Instrumentation and Data Processing, 54(6), 582-599. https://doi.org/10.3103/S8756699018060079

Vancouver

Borzov SM, Potaturkin OI. Spectral-Spatial Methods for Hyperspectral Image Classification. Review. Optoelectronics, Instrumentation and Data Processing. 2018 нояб. 1;54(6):582-599. doi: 10.3103/S8756699018060079

Author

Borzov, S. M. ; Potaturkin, O. I. / Spectral-Spatial Methods for Hyperspectral Image Classification. Review. в: Optoelectronics, Instrumentation and Data Processing. 2018 ; Том 54, № 6. стр. 582-599.

BibTeX

@article{df9134f11dff4b9ba0a99b016f00d5cf,
title = "Spectral-Spatial Methods for Hyperspectral Image Classification. Review",
abstract = "Various methods of spectral-spatial classification of hyperspectral data are reviewed. Papers devoted to the most popular ways of using spatial information for increasing the accuracy of classification maps are considered. It is shown that the best results are obtained by using preprocessing of “raw” data before the procedures of pixel-wise spectral classification. Disadvantages, limits, and possible directions for developing existing methods are investigated and analyzed.",
keywords = "hyperspectral images, remote sensing, spectral and spatial features, surface type classification, ATTRIBUTE PROFILES, SEGMENTATION, SVMS",
author = "Borzov, {S. M.} and Potaturkin, {O. I.}",
year = "2018",
month = nov,
day = "1",
doi = "10.3103/S8756699018060079",
language = "English",
volume = "54",
pages = "582--599",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "6",

}

RIS

TY - JOUR

T1 - Spectral-Spatial Methods for Hyperspectral Image Classification. Review

AU - Borzov, S. M.

AU - Potaturkin, O. I.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Various methods of spectral-spatial classification of hyperspectral data are reviewed. Papers devoted to the most popular ways of using spatial information for increasing the accuracy of classification maps are considered. It is shown that the best results are obtained by using preprocessing of “raw” data before the procedures of pixel-wise spectral classification. Disadvantages, limits, and possible directions for developing existing methods are investigated and analyzed.

AB - Various methods of spectral-spatial classification of hyperspectral data are reviewed. Papers devoted to the most popular ways of using spatial information for increasing the accuracy of classification maps are considered. It is shown that the best results are obtained by using preprocessing of “raw” data before the procedures of pixel-wise spectral classification. Disadvantages, limits, and possible directions for developing existing methods are investigated and analyzed.

KW - hyperspectral images

KW - remote sensing

KW - spectral and spatial features

KW - surface type classification

KW - ATTRIBUTE PROFILES

KW - SEGMENTATION

KW - SVMS

UR - http://www.scopus.com/inward/record.url?scp=85060816191&partnerID=8YFLogxK

U2 - 10.3103/S8756699018060079

DO - 10.3103/S8756699018060079

M3 - Article

AN - SCOPUS:85060816191

VL - 54

SP - 582

EP - 599

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 6

ER -

ID: 25325400