Automated ground-based cloud recognition

Recognition of naturally occurring objects is a challenging task. In particular, the recognition of clouds is particularly challenging as the texture of such objects is extremely variable under different atmospheric conditions. There are several benefits of a practical system that can detect and recognise clouds in natural images especially for applications such as air traffic control. In this paper, we test well-known texture feature extraction approaches for automatically training a classifier system to recognise cumulus, towering cumulus, cumulo-nimbus clouds, sky and other clouds. For cloud recognition, we use a total of five different feature extraction methods, namely autocorrelation, co-occurrence matrices, edge frequency, Law's features and primitive length. We use the k-nearest neighbour and neural network classifiers for identifying cloud types in test images. This exhaustive testing gives us a better understanding of the strengths and limitations of different feature extraction methods and classification techniques on the given problem. In particular, we find that no single feature extraction method is best suited for recognising all classes. Each method has its own merits. We discuss these merits individually and suggest further improvements in this difficult area.