Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition

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.

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

“…Other ground-based sky imaging designs have also been developed (Seiz et al, 2007;Souza-Echer et al, 2006;Calbo and Sabburg, 2008;Cazorla et al, 2008;Heinle et al, 2010;Román et al, 2012;Gauchet et al, 2012) with the most dissimilar design consisting of a downward-pointing camera capturing the sky from a reflection off a spherical mirror (Pfister et al, 2003;Kassianov et al, 2005;Long et al, 2006;Mantelli et al, 2010;Martínez-Chico et al, 2011). Most ground imaging devices follow a relationship between the camera's signal and radiance similar to Eq.…”

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

“…In some images, detection of the skyline was difficult. The angle from which the image was obtained also had a significant effect on skyline detection [43]. …”

Block-based cloud classification with statistical features and distribution of local texture features

“…As a consequence, devices that capture all-sky images are designed to monitor the sun and the clouds. Devices developed more recently include a whole-sky imager developed by Scripps Institute of Oceanography at the University of California (Li et al, 2004;Kassianov et al, 2005), a whole-sky camera designed by Spain's University of Girona (Long et al, 2006), an all-sky imager developed by Japan's Communications Research Laboratory (Kubota et al, 2003), and a total-sky imager by Yankee Environmental Systems (Pfister et al, 2003;Calbo and Sabburg, 2008). With the all-sky images captured by these devices, analyzing the cloud activities on a basis of more refined scales is feasible.…”