Standard

Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning. / Berikov, Vladimir B.; Pestunov, Igor A.; Karaev, Nikita M. et al.

In: CEUR Workshop Proceedings, Vol. 2033, 2017, p. 60-64.

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

Harvard

Berikov, VB, Pestunov, IA, Karaev, NM & Tewari, A 2017, 'Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning', CEUR Workshop Proceedings, vol. 2033, pp. 60-64.

APA

Berikov, V. B., Pestunov, I. A., Karaev, N. M., & Tewari, A. (2017). Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning. CEUR Workshop Proceedings, 2033, 60-64.

Vancouver

Berikov VB, Pestunov IA, Karaev NM, Tewari A. Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning. CEUR Workshop Proceedings. 2017;2033:60-64.

Author

Berikov, Vladimir B. ; Pestunov, Igor A. ; Karaev, Nikita M. et al. / Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning. In: CEUR Workshop Proceedings. 2017 ; Vol. 2033. pp. 60-64.

BibTeX

@article{990fbadc6d9d4a3f8c66e77e7a2e370a,
title = "Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning",
abstract = "We suggest a method for hyperspectral image analysis on the basis of semi-supervised learning. The main idea is to divide the process of training of a classifier into two stages. First of all, with usage of cluster ensemble algorithms, variants of image segmentation are obtained. On their basis, the averaged co-Association matrix is calculated. On the second stage, a classifier is constructed on labeled pixels using similarity based learning algorithms with the given matrix as input. An example of the application of the method for analysis of hyperspectral images is given. It is shown that the suggested algorithm is more robust to noise than the standard support vector machine method.",
keywords = "Cluster ensemble, Hyperspectral image, Learning by similarity, Semi-supervised learning",
author = "Berikov, {Vladimir B.} and Pestunov, {Igor A.} and Karaev, {Nikita M.} and Ankit Tewari",
year = "2017",
language = "English",
volume = "2033",
pages = "60--64",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR-WS",

}

RIS

TY - JOUR

T1 - Recognition of hyperspectral images with use of cluster ensemble and semisupervised learning

AU - Berikov, Vladimir B.

AU - Pestunov, Igor A.

AU - Karaev, Nikita M.

AU - Tewari, Ankit

PY - 2017

Y1 - 2017

N2 - We suggest a method for hyperspectral image analysis on the basis of semi-supervised learning. The main idea is to divide the process of training of a classifier into two stages. First of all, with usage of cluster ensemble algorithms, variants of image segmentation are obtained. On their basis, the averaged co-Association matrix is calculated. On the second stage, a classifier is constructed on labeled pixels using similarity based learning algorithms with the given matrix as input. An example of the application of the method for analysis of hyperspectral images is given. It is shown that the suggested algorithm is more robust to noise than the standard support vector machine method.

AB - We suggest a method for hyperspectral image analysis on the basis of semi-supervised learning. The main idea is to divide the process of training of a classifier into two stages. First of all, with usage of cluster ensemble algorithms, variants of image segmentation are obtained. On their basis, the averaged co-Association matrix is calculated. On the second stage, a classifier is constructed on labeled pixels using similarity based learning algorithms with the given matrix as input. An example of the application of the method for analysis of hyperspectral images is given. It is shown that the suggested algorithm is more robust to noise than the standard support vector machine method.

KW - Cluster ensemble

KW - Hyperspectral image

KW - Learning by similarity

KW - Semi-supervised learning

UR - http://www.scopus.com/inward/record.url?scp=85040226054&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85040226054

VL - 2033

SP - 60

EP - 64

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -

ID: 9670990