The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - May/June (2010 vol.25)
pp: 86-91
David S. Hayden , Jet Propulsion Laboratory
David R. Thompson , Jet Propulsion Laboratory
Rebecca Castano , Jet Propulsion Laboratory
ABSTRACT
<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>
INDEX TERMS
Jet Propulsion Laboratory, NASA, image clustering, autonomous image classification, intelligent systems
CITATION
David S. Hayden, David R. Thompson, Rebecca Castano, "Using Onboard Clustering to Summarize Remotely Sensed Imagery", IEEE Intelligent Systems, vol.25, no. 3, pp. 86-91, May/June 2010, doi:10.1109/MIS.2010.90
REFERENCES
1. "2006 Solar System Exploration Roadmap," tech. report, NASA, 2006.
2. J. Hall et al., "An Aerobot for Global In Situ Exploration of Titan," Advances in Space Research, vol. 37, no. 11, 2006, pp. 2108–2119.
3. R. Castano et al., "Opportunistic Rover Science: Finding and Reacting to Rocks, Clouds and Dust Devils," Proc. IEEE Aerospace Conf., IEEE Press, 2006, p. 16.
4. S. Chien et al., "Using Autonomy Flight Software to Improve Science Return on Earth Observing One," J. Aerospace Computing, Information, and Communication, AIAA, 2005, pp. 196–216.
5. R. Castano et al., "Oasis: Onboard Autonomous Science Investigation System for Opportunistic Rover Science: Research Articles," J. Field Robotics, vol. 24, no. 5, 2007, pp. 379–397.
6. D. Thompson, T. Smith, and D. Wettergreen, "Information-Optimal Selective Data Return for Autonomous Rover Traverse Science and Survey," Proc. IEEE Int'l Conf. Robotics and Automation (ICRA 08), 2008, pp. 968–973.
7. Y. Chen, J.Z. Wang, and R. Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE Trans. Image Processing, vol. 14, no. 8, 2004, pp. 1187–1201.
8. L.S. Kennedy and M. Naaman, "Generating Diverse and Representative Image Search Results for Landmarks," Proc. 17th Int'l Conf. World Wide Web, ACM Press, 2008, pp. 297–306.
9. S. Krishnamachari and M. Abdel-Mottaleb, "Image Browsing Using Hierarchical Clustering," Proc. 4th IEEE Symp. Computers and Communications (ISCC), IEEE CS Press, 1999, pp. 301–309.
10. K.R. Harvey and G.J.E. Hill, "Vegetation Mapping of a Tropical Freshwater Swamp in the Northern Territory, Australia: A Comparison of Aerial Photography, Landsat TM, and SPOT Satellite Imagery," Int'l J. Remote Sensing, vol. 22, no. 15, 2001, pp. 2911–2925.
11. P.R. Christensen et al., "Planetary Data System Node," THEMIS public data releases, Arizona State Univ., http://themis-data.asu.edu.
12. S. Liu and M.E. Jernigan, "Texture Analysis and Discrimination in Additive Noise," Computer Vision, Graphics, and Image Processing, vol. 49, no. 1, 1990, pp. 52–67.
13. S. Lloyd, "Least Squares Quantization in PCM," IEEE Trans. Information Theory, vol. 28, no. 2, 1982, pp. 129–137.
14. N.X. Vinh, J. Epps, and J. Bailey, "Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?" Proc. 26th Ann. Int'l Conf. Machine Learning, ACM Press, 2009, pp. 1073–1080.
15. L. Hubert and P. Arabie, "Comparing partitions," J. Classification, vol. 2, Dec. 1985, pp. 193–218.
16. J.W. Cooley and J.W. Tukey, "An Algorithm for the Machine Calculation of Complex Fourier Series," Mathematics of Computation, vol. 19, Apr. 1965, pp. 297–301.
17. D. Arthur and S. Vassilvitskii, "k-Means++: The Advantages of Careful Seeding," Proc. 18th Ann. ACM-SIAM Symp. Discrete Algorithms (SODA 07), Soc. for Industrial and Applied Mathematics, 2007, pp. 1035, 1027.
7 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool