From pixels to patches: a cloud classification method based on bag of micro-structures

Abstract

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. And then it constructs an image representation with a weighted histogram of micro-structures. Lastly, a support vector machine (SVM) classifier is applied on the image representation because SVM is appealing for sparse and high dimensional feature space. Five different sky conditions are identified: cirriform, cumuliform, stratiform, clear sky and mixed cloudiness that often appears in all-sky images but is seldom addressed in literature. BoMS is evaluated on a large dataset, which contains 5000 all-sky images that are captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10 fold cross-validation, and it outperforms the state-of-the-art method with an increase of about 19 %. Furthermore, influence of key parameters in BoMS are investigated to verify their robustness.