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Identification of clouds and aurorae in optical data images

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Identification of clouds and aurorae in optical data images. / Seviour, Rebecca; Kosch, M.; Honary, F.
In: New Journal of Physics, Vol. 5, 17.01.2003, p. 6.1-6.7.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Seviour, R, Kosch, M & Honary, F 2003, 'Identification of clouds and aurorae in optical data images', New Journal of Physics, vol. 5, pp. 6.1-6.7. https://doi.org/10.1088/1367-2630/5/1/306

APA

Seviour, R., Kosch, M., & Honary, F. (2003). Identification of clouds and aurorae in optical data images. New Journal of Physics, 5, 6.1-6.7. https://doi.org/10.1088/1367-2630/5/1/306

Vancouver

Seviour R, Kosch M, Honary F. Identification of clouds and aurorae in optical data images. New Journal of Physics. 2003 Jan 17;5:6.1-6.7. doi: 10.1088/1367-2630/5/1/306

Author

Seviour, Rebecca ; Kosch, M. ; Honary, F. / Identification of clouds and aurorae in optical data images. In: New Journal of Physics. 2003 ; Vol. 5. pp. 6.1-6.7.

Bibtex

@article{4fbc0f7566bb4b01bdf4241e6306c298,
title = "Identification of clouds and aurorae in optical data images",
abstract = "In this paper we present an automatic image recognition technique used to identify clouds and aurorae in digital images, taken with a CCD all-sky imager. The image recognition algorithm uses image segmentation to generate a binary block object image. Object analysis is then performed on the binary block image, the results of which are used to assess whether clouds, aurorae and stars are present in the original image. The need for such an algorithm arises because the optical study of particle precipitation into the Earth's atmosphere by the Ionosphere and Radio Propagation Group at Lancaster generates vast data-sets, over 25 000 images/year, making manual classification of all the images impractical.",
keywords = "all-sky camera DCS-publications-id, art-378, DCS-publications-credits, dasi, iono-fa, scasi, DCS-publications-personnel-id, 50, 7, 5",
author = "Rebecca Seviour and M. Kosch and F. Honary",
note = "The final, definitive version of this article has been published in the Journal, New Journal of Physics, 5, pp 6.1-6.7 2003, {\textcopyright} 2003 Institute of Physics.",
year = "2003",
month = jan,
day = "17",
doi = "10.1088/1367-2630/5/1/306",
language = "English",
volume = "5",
pages = "6.1--6.7",
journal = "New Journal of Physics",
issn = "1367-2630",
publisher = "IOP Publishing Ltd",

}

RIS

TY - JOUR

T1 - Identification of clouds and aurorae in optical data images

AU - Seviour, Rebecca

AU - Kosch, M.

AU - Honary, F.

N1 - The final, definitive version of this article has been published in the Journal, New Journal of Physics, 5, pp 6.1-6.7 2003, © 2003 Institute of Physics.

PY - 2003/1/17

Y1 - 2003/1/17

N2 - In this paper we present an automatic image recognition technique used to identify clouds and aurorae in digital images, taken with a CCD all-sky imager. The image recognition algorithm uses image segmentation to generate a binary block object image. Object analysis is then performed on the binary block image, the results of which are used to assess whether clouds, aurorae and stars are present in the original image. The need for such an algorithm arises because the optical study of particle precipitation into the Earth's atmosphere by the Ionosphere and Radio Propagation Group at Lancaster generates vast data-sets, over 25 000 images/year, making manual classification of all the images impractical.

AB - In this paper we present an automatic image recognition technique used to identify clouds and aurorae in digital images, taken with a CCD all-sky imager. The image recognition algorithm uses image segmentation to generate a binary block object image. Object analysis is then performed on the binary block image, the results of which are used to assess whether clouds, aurorae and stars are present in the original image. The need for such an algorithm arises because the optical study of particle precipitation into the Earth's atmosphere by the Ionosphere and Radio Propagation Group at Lancaster generates vast data-sets, over 25 000 images/year, making manual classification of all the images impractical.

KW - all-sky camera DCS-publications-id

KW - art-378

KW - DCS-publications-credits

KW - dasi

KW - iono-fa

KW - scasi

KW - DCS-publications-personnel-id

KW - 50

KW - 7

KW - 5

U2 - 10.1088/1367-2630/5/1/306

DO - 10.1088/1367-2630/5/1/306

M3 - Journal article

VL - 5

SP - 6.1-6.7

JO - New Journal of Physics

JF - New Journal of Physics

SN - 1367-2630

ER -