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Query-by-shape in meteorological image archives using the point diffusion technique

The authors work on meteorological satellite image archives and provide a novel and useful query-by-shape tool. To this aim, they first present the point diffusion technique (PDT), a fast and efficient method for shape similarity evaluation. Thanks to its very structure, this approach is suitable to handle objects whose shape is not well defined and can be represented by a set of sparse points. PDT is thus suitable for application to similarity-based retrieval from remotely sensed image archives, where shapes are hardly defined but are still among the major features of interest. Moreover, they prove here that PDT is almost as effective as more standard procedures for shape-based database queries, although significantly faster. In other words, it manages to combine retrieval speed and precision, the features of greatest importance for a first remote sensing data prescreening in many applications. Archives of meteorological satellite images are typical examples of very large-sized, remote sensing-based databases with a special attention for shape features. Each meteorological satellite produces terabytes of data every day, a large part of which is not immediately analyzed and ends being stored in archives. The application of PDT to such a database is presented and discussed, and a comparison with a standard method developed for meteorological shape analysis is provided.