FFT features and hierarchical classification algorithms for cloud images

Cloud-type recognition is useful in preventing losses caused by adverse weather conditions. This paper presents a methodology and algorithms for automatic recognition of cloud types from color cloud images taken from the ground. We propose a number of eight algorithms for automatic cloud classification of seven cloud types defined by meteorological organization. Recognition is based on a number of features extracted from images which are related to color, texture, and shape. We introduce three new features based on the Fourier transform, namely, the modified k-FFTPX, the half k-FFTPX, and the h×k-FFT. The classification technique is based on artificial neural network (ANN) with tree algorithm to extract features. The proposed classification tree algorithm uses the technique called hierarchical classification which is composed of three levels of tree. Each level of the tree is capable of classifying up to four classes. We show that this method provides the highest accuracy at 98.08% through a series of four experiments. For accuracy assessment, each experiment splits dataset into train and test images using Leave-One-Out Cross-Validation (LOOCV). The result confirms that the hierarchical classification performs better than a single classification. In addition, the tree can be adapted to classify lesser number of cloud types. Our experiment reveals that the accuracy for classifying two classes, cloud and no-cloud, is high as 100%.