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Classification of Hyperspectral Images with Different Methods of Training Set Formation. / Borzov, S. M.; Potaturkin, O. I.

In: Optoelectronics, Instrumentation and Data Processing, Vol. 54, No. 1, 01.01.2018, p. 76-82.

Research output: Contribution to journal › Article › peer-review

Harvard

Borzov, SM & Potaturkin, OI 2018, 'Classification of Hyperspectral Images with Different Methods of Training Set Formation', Optoelectronics, Instrumentation and Data Processing, vol. 54, no. 1, pp. 76-82. https://doi.org/10.3103/S8756699018010120

APA

Borzov, S. M., & Potaturkin, O. I. (2018). Classification of Hyperspectral Images with Different Methods of Training Set Formation. Optoelectronics, Instrumentation and Data Processing, 54(1), 76-82. https://doi.org/10.3103/S8756699018010120

Vancouver

Borzov SM, Potaturkin OI. Classification of Hyperspectral Images with Different Methods of Training Set Formation. Optoelectronics, Instrumentation and Data Processing. 2018 Jan 1;54(1):76-82. doi: 10.3103/S8756699018010120

Author

Borzov, S. M. ; Potaturkin, O. I. / Classification of Hyperspectral Images with Different Methods of Training Set Formation. In: Optoelectronics, Instrumentation and Data Processing. 2018 ; Vol. 54, No. 1. pp. 76-82.

BibTeX

@article{a9ee7f84426842dd87df9d24d13d080e,
title = "Classification of Hyperspectral Images with Different Methods of Training Set Formation",
abstract = "The efficiency of the methods of controlled spectral and spectral-spatial classification of vegetation types on the basis of hyperspectral pictures with different methods of training set formation is evaluated. The dependence of the classification accuracy on the number of spectral features is considered. It is shown that simultaneous allowance for spatial and spectral features ensures highquality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.",
keywords = "classification of surface types, hyperspectral image, remote sensing, spectral and spatial features",
author = "Borzov, {S. M.} and Potaturkin, {O. I.}",
year = "2018",
month = jan,
day = "1",
doi = "10.3103/S8756699018010120",
language = "English",
volume = "54",
pages = "76--82",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Classification of Hyperspectral Images with Different Methods of Training Set Formation

AU - Borzov, S. M.

AU - Potaturkin, O. I.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - The efficiency of the methods of controlled spectral and spectral-spatial classification of vegetation types on the basis of hyperspectral pictures with different methods of training set formation is evaluated. The dependence of the classification accuracy on the number of spectral features is considered. It is shown that simultaneous allowance for spatial and spectral features ensures highquality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.

AB - The efficiency of the methods of controlled spectral and spectral-spatial classification of vegetation types on the basis of hyperspectral pictures with different methods of training set formation is evaluated. The dependence of the classification accuracy on the number of spectral features is considered. It is shown that simultaneous allowance for spatial and spectral features ensures highquality classification of similarly looking types of vegetation by merely using training sets with the maximum degree of the pixel distribution over the image.

KW - classification of surface types

KW - hyperspectral image

KW - remote sensing

KW - spectral and spatial features

UR - http://www.scopus.com/inward/record.url?scp=85044840104&partnerID=8YFLogxK

U2 - 10.3103/S8756699018010120

DO - 10.3103/S8756699018010120

M3 - Article

AN - SCOPUS:85044840104

VL - 54

SP - 76

EP - 82

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 1

ER -

ID: 12475493