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Efficiency of the spectral-spatial classification of hyperspectral imaging data. / Borzov, S. M.; Potaturkin, O. I.

In: Optoelectronics, Instrumentation and Data Processing, Vol. 53, No. 1, 01.01.2017, p. 26-34.

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

Harvard

Borzov, SM & Potaturkin, OI 2017, 'Efficiency of the spectral-spatial classification of hyperspectral imaging data', Optoelectronics, Instrumentation and Data Processing, vol. 53, no. 1, pp. 26-34. https://doi.org/10.3103/S8756699017010058

APA

Borzov, S. M., & Potaturkin, O. I. (2017). Efficiency of the spectral-spatial classification of hyperspectral imaging data. Optoelectronics, Instrumentation and Data Processing, 53(1), 26-34. https://doi.org/10.3103/S8756699017010058

Vancouver

Borzov SM, Potaturkin OI. Efficiency of the spectral-spatial classification of hyperspectral imaging data. Optoelectronics, Instrumentation and Data Processing. 2017 Jan 1;53(1):26-34. doi: 10.3103/S8756699017010058

Author

Borzov, S. M. ; Potaturkin, O. I. / Efficiency of the spectral-spatial classification of hyperspectral imaging data. In: Optoelectronics, Instrumentation and Data Processing. 2017 ; Vol. 53, No. 1. pp. 26-34.

BibTeX

@article{bfd3d66deeb84b2eb28c58ed008ddd72,
title = "Efficiency of the spectral-spatial classification of hyperspectral imaging data",
abstract = "The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.",
keywords = "classification of underlying surface types, fragments, hyperspectral images, remote sensing, spectral and spatial features",
author = "Borzov, {S. M.} and Potaturkin, {O. I.}",
year = "2017",
month = jan,
day = "1",
doi = "10.3103/S8756699017010058",
language = "English",
volume = "53",
pages = "26--34",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Efficiency of the spectral-spatial classification of hyperspectral imaging data

AU - Borzov, S. M.

AU - Potaturkin, O. I.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

AB - The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

KW - classification of underlying surface types

KW - fragments

KW - hyperspectral images

KW - remote sensing

KW - spectral and spatial features

UR - http://www.scopus.com/inward/record.url?scp=85018508518&partnerID=8YFLogxK

U2 - 10.3103/S8756699017010058

DO - 10.3103/S8756699017010058

M3 - Article

AN - SCOPUS:85018508518

VL - 53

SP - 26

EP - 34

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 1

ER -

ID: 10522333