Spectral-Spatial Methods for Hyperspectral Image Classification. Review

S. M. Borzov; O. I. Potaturkin

doi:10.3103/S8756699018060079

Spectral-Spatial Methods for Hyperspectral Image Classification. Review

S. M. Borzov, O. I. Potaturkin

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

7 Citations (Scopus)

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.

Original language	English
Pages (from-to)	582-599
Number of pages	18
Journal	Optoelectronics, Instrumentation and Data Processing
Volume	54
Issue number	6
DOIs	https://doi.org/10.3103/S8756699018060079
Publication status	Published - 1 Nov 2018
Externally published	Yes

Keywords

hyperspectral images
remote sensing
spectral and spatial features
surface type classification
ATTRIBUTE PROFILES
SEGMENTATION
SVMS

Access to Document

10.3103/S8756699018060079

Cite this

@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",

}

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 -

Spectral-Spatial Methods for Hyperspectral Image Classification. Review

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this