Feature extraction from whole-sky ground-based images for cloud-type recognition

Article


Calbo, Josep and Sabburg, Jeff. 2008. "Feature extraction from whole-sky ground-based images for cloud-type recognition." Journal of Atmospheric and Oceanic Technology. 25 (1), pp. 3-14. https://doi.org/10.1175/2007JTECHA959.1
Article Title

Feature extraction from whole-sky ground-based images for cloud-type recognition

ERA Journal ID1920
Article CategoryArticle
AuthorsCalbo, Josep (Author) and Sabburg, Jeff (Author)
Journal TitleJournal of Atmospheric and Oceanic Technology
Journal Citation25 (1), pp. 3-14
Number of Pages12
Year2008
Place of PublicationBoston, MA. USA
ISSN0739-0572
1520-0426
Digital Object Identifier (DOI)https://doi.org/10.1175/2007JTECHA959.1
Web Address (URL)http://journals.ametsoc.org/doi/pdf/10.1175/2007JTECHA959.1
Abstract

Several features that can be extracted from digital images of the sky and that can be useful for cloud-type classification of such images are presented. Some features are statistical measurements of image texture, some are based on the Fourier transform of the image and, finally, others are computed from the image where cloudy pixels are distinguished from clear-sky pixels. The use of the most suitable features in an automatic classification algorithm is also shown and discussed. Both the features and the classifier are developed over images taken by two different camera devices, namely, a total sky imager (TSI) and a whole sky imager (WSC), which are placed in two different areas of the world (Toowoomba, Australia; and Girona, Spain, respectively). The performance of the classifier is assessed by comparing its image classification with an a priori classification carried out by visual inspection of more than 200 images from each camera. The index of agreement is 76% when five different sky conditions are considered: clear, low cumuliform clouds, stratiform clouds (overcast), cirriform clouds, and mottled clouds (altocumulus, cirrocumulus). Discussion on the future directions of this research is also presented, regarding both the use of other features and the use of other classification techniques.

Keywordsclouds; algorithms; instrumentation; classification
ANZSRC Field of Research 2020370108. Meteorology
401304. Photogrammetry and remote sensing
370107. Cloud physics
Byline AffiliationsUniversity of Girona, Spain
Department of Biological and Physical Sciences
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