Novel approach to identify good tracer clouds from a sequence of satellite images

A novel hierarchical method for finding tracer clouds from weather satellite images is proposed. From the sequence of cloud images, different features such as mean, standard deviation, busyness, and entropy are extracted. Based on these features, clouds are segmented using the k-means clustering algorithm and considering the coldest cloud segment, potential regions for tracer clouds are identified. These regions are represented by a set of features. All such steps are repeated for images taken at three consecutive time instants. Then, simulated annealing is used to establish an association between cloud segments of successive image frames. In this way, several chains of associated cloud regions are found and are ranked using fuzzy reasoning. The method has been tested in several image sequences, and its results are validated by determining cloud motion vector from the associated chains of tracers.