Mr. Mike J. Brettle Profile

Mike J. Brettle

at Campbell Scientific Ltd

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Proceedings Article | 17 October 2013 Paper

Automated cloud classification using a ground based infra-red camera and texture analysis techniques

Emal Rumi, David Kerr, Jeremy Coupland, Andrew Sandford, Mike Brettle

Proceedings Volume 8890, 88900J (2013) https://doi.org/10.1117/12.2025238

KEYWORDS: Clouds, Cameras, Infrared imaging, Infrared cameras, Image classification, Image filtering, Thermography, Sun, Visible radiation, Infrared radiation

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Clouds play an important role in influencing the dynamics of local and global weather and climate conditions. Continuous monitoring of clouds is vital for weather forecasting and for air-traffic control. Convective clouds such as Towering Cumulus (TCU) and Cumulonimbus clouds (CB) are associated with thunderstorms, turbulence and atmospheric instability. Human observers periodically report the presence of CB and TCU clouds during operational hours at airports and observatories; however such observations are expensive and time limited. Robust, automatic classification of cloud type using infrared ground-based instrumentation offers the advantage of continuous, real-time (24/7) data capture and the representation of cloud structure in the form of a thermal map, which can greatly help to characterise certain cloud formations. The work presented here utilised a ground based infrared (8-14 μm) imaging device mounted on a pan/tilt unit for capturing high spatial resolution sky images. These images were processed to extract 45 separate textural features using statistical and spatial frequency based analytical techniques. These features were used to train a weighted k-nearest neighbour (KNN) classifier in order to determine cloud type. Ground truth data were obtained by inspection of images captured simultaneously from a visible wavelength colour camera at the same installation, with approximately the same field of view as the infrared device. These images were classified by a trained cloud observer. Results from the KNN classifier gave an encouraging success rate. A Probability of Detection (POD) of up to 90% with a Probability of False Alarm (POFA) as low as 16% was achieved.

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