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Issue No. 03 - May/June (2010 vol. 25)
ISSN: 1541-1672
pp: 86-91
David S. Hayden , Jet Propulsion Laboratory
Steve Chien , Jet Propulsion Laboratory
David R. Thompson , Jet Propulsion Laboratory
Rebecca Castano , Jet Propulsion Laboratory
<p>Many current and future space missions can collect far more data than can be returned to Earth for analysis by scientists. To address this situation, the Jet Propulsion Laboratory has developed computationally efficient algorithms for analyzing science imagery onboard spacecraft. These algorithms autonomously cluster the data into classes of similar imagery, enabling selective downlink of representatives of each class and a map classifying the imaged terrain rather than the full data set, reducing data volume downlinked. This article demonstrates the method on terrestrial imagery. The authors examine a range of approaches including k-means clustering using image features based on color, texture, temporal, and spatial arrangement. They demonstrate the potential for such summarization algorithms to enable effective exploratory science despite limited downlink bandwidth.</p>
Jet Propulsion Laboratory, NASA, image clustering, autonomous image classification, intelligent systems

S. Chien, D. S. Hayden, D. R. Thompson and R. Castano, "Using Onboard Clustering to Summarize Remotely Sensed Imagery," in IEEE Intelligent Systems, vol. 25, no. , pp. 86-91, 2010.
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