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Issue No.04 - July/August (2012 vol.14)
pp: 92-97
Nargess Memarsadeghi , NASA Goddard Space Flight Center
Thomas Doggett , Northern Virginia Community College
ABSTRACT
This case study shows how remotely sensed hyperspectral data of NASA's Earth Observing-1 satellite are processed to detect features such as ice, water, and snow on Earth.
INDEX TERMS
Classification algorithms, Remote sensing, Earth, Clustering algorithms, Change detection algorithms, Hyperspectral sensors, Earth Observing-1, Classification algorithms, Remote sensing, Earth, Clustering algorithms, Change detection algorithms, Hyperspectral sensors, classification, remote sensing, hyperspectral data processing
CITATION
Nargess Memarsadeghi, Thomas Doggett, "NASA Computational Case Study, Hyperspectral Data Processing: Cryospheric Change Detection", Computing in Science & Engineering, vol.14, no. 4, pp. 92-97, July/August 2012, doi:10.1109/MCSE.2012.43
REFERENCES
1. F.F. Sabins, Remote Sensing Principles and Interpretation, W.H. Freeman 3rd ed., 1996.
2. M.K. Griffn et al., “Examples of EO-1 Hyperion Data Analysis,” Lincoln Laboratory J., vol. 15, no. 2, 2005, pp. 271–298.
3. J.A. Richards, and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, Springer, 4th ed., 2005.
4. P. Barry, EO-1/Hyperion Science Data User's Guide, Level 1-B, TRW Space, Defense, and Information Systems., Redondo Beach, CA, Nov. 2001; www.cossa.csiro.au/hswww/oz_pi/docsEO1_DUG.PDF .
5. T. Doggett et al., “Autonomous Detection of Cryospheric Change with Hyperion On-Board Earth Observing-1,” Remote Sensing of Environment, vol. 101, no. 4, 2006, pp. 447–462.
6. N.M. Short Sr., Remote Sensing Tutorial, 2012; http:/rst.gsfc.nasa.gov.
7. NASA, Landsat 7 Science Data Users Handbook, ch. 11; http://landsathandbook.gsfc.nasa.gov/pdfs Landsat7_Handbook.pdf.
8. Naval Meteorology and Oceanography Command, “Sun or Moon Altitude/Azimuth Table: U.S. Cities and Towns,” Naval Oceanography Portal; www.usno.navy.mil/USNO/astronomical-applications/ data-servicesalt-az-us.
9. A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988.
10. N. Memarsadeghi and D.P. O'Leary, “Classified Information: The Data Clustering Problem,” Scientific Computing with Case Studies, ch. 11, Soc. for Industrial and Applied Mathematics (SIAM), 2009, pp. 149–155 (also appeared in Computing in Science & Eng., vol. 5, no. 5, 2003, pp. 54–57; and Computing in Science & Eng., vol. 5, no. 6, 2003, pp. 64–70).
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