The Penn State Research Foundation

The Penn State University

Yu Zhang

James Z Wang

Jia Li

Michael Alan Steinberg

Stephen Wistar

Satellite images from vast historical archives are analyzed to predict severe storms. We extract and summarize important visual storm evidence from satellite image sequences in a way similar to how meteorologists interpret these images. The method extracts and fits local cloud motions from image sequences to model the storm-related cloud patches. Image data of an entire year are adopted to train the model. The historical storm reports since the year 2000 are used as the ground-truth and statistical priors in the modeling process. Experiments demonstrate the usefulness and potential of the algorithm for producing improved storm forecasts. A preferred method applies cloud motion estimation in image sequences. This aspect of the invention is important because it extracts and models certain patterns of cloud motion, in addition to capturing the cloud displacement.

Identifying visual storm signatures from satellite images

The trend of analyzing big data in artificial intelligence requires more scalable machine learning algorithms, among which clustering is a fundamental and arguably the most widely applied method. To extend the applications of regular vector-based clustering algorithms, the Discrete Distribution (D2) clustering algorithm has been developed for clustering bags of weighted vectors which are well adopted in many emerging machine learning applications. The high computational complexity of D2-clustering limits its impact in solving massive learning problems. Here we present a parallel D2-clustering algorithm with substantially improved scalability. We develop a hierarchical structure for parallel computing in order to achieve a balance between the individual-node computation and the integration process of the algorithm. The parallel algorithm achieves significant speed-up with minor accuracy loss.

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Chicago, IL, United States of America

Yu Zhang