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Proceedings Paper

Content-based event selection from satellite images of the aurora
Author(s): Ramin Samadani; Cecilia S. Han; Lalitesh K. Katragadda
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Paper Abstract

The DE-1 satellite has gathered over 500,000 images of the Earth's aurora. This data set provides a realistic testbed for developing algorithms for scientific image databases. Scientists studying the aurora currently need to browse through large numbers of images to find events suitable for further scientific studies. They select or reject an image based on a variety of visual queues, including shape, size, and intensity. This paper describes a system currently under development for selecting interesting events based on image content. We use boundaries from the images to outline the aurora, and then to extract features that relate to shape, size, and intensity. These features are then input into a supervised decision tree classifier. The system retrieves images of potential interest to the user. The user makes the final decision regarding the use of the images retrieved. The algorithm is applied to hundreds of DE-1 satellite images to find `quiet' versus `active' auroras, after being initially trained by the user. The system's advantage over neural networks is that the scientists may inspect the event selection process by studying the decision tree generated.

Paper Details

Date Published: 14 April 1993
PDF: 10 pages
Proc. SPIE 1908, Storage and Retrieval for Image and Video Databases, (14 April 1993); doi: 10.1117/12.143655
Show Author Affiliations
Ramin Samadani, Stanford Univ. (United States)
Cecilia S. Han, Stanford Univ. (United States)
Lalitesh K. Katragadda, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 1908:
Storage and Retrieval for Image and Video Databases
Carlton Wayne Niblack, Editor(s)

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