NASA’s planetary missions have collected, and continue to collect, massive volumes of orbital imagery. The volume is such that it is difficult to manually review all of the data and determine its significance. As a result, images are indexed and searchable by location and date but generally not by their content. A new automated method analyzes images and identifies “landmarks,” or visually salient features such as gullies, craters, dust devil tracks, and the like. This technique uses a statistical measure of salience derived from information theory, so it is not associated with any specific landmark type. It identifies regions that are unusual or that stand out from their surroundings, so the resulting landmarks are context-sensitive areas that can be used to recognize the same area when it is encountered again.
This method enables fast, automated identification of landmarks to augment or replace manual analysis; fast, automated classification of landmarks to provide semantic annotations; and content-based searches over image archives.
Automated landmark detection in images permits the creation of a summary catalog of all such features in an image database, such as the Planetary Data System (PDS). It could enable entirely new searches for PDS images, based on the desired content (landmark types). In the near future, landmark identification methods using Gabor filters (texture) or covariance descriptors will also be investigated for this application.
This work was done by Kiri L. Wagstaff and Julian Panetta of Caltech; Norbert Schorghofer of the University of Hawaii; and Ronald Greeley, Mary Pendleton Hoffer, and Melissa Bunte of Arizona State University for NASA’s Jet Propulsion Laboratory. For more information, download the Technical Support Package (free white paper) at www.techbriefs.com/tsp under the Information Sciences category. NPO-46674
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Landmark Detection In Orbital Images Using Salience Histograms (reference NPO-46674) is currently available for download from the TSP library.
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