Image analysis – Pattern recognition – Feature extraction
Reexamination Certificate
2006-08-01
2006-08-01
Bali, Vikkram (Department: 2623)
Image analysis
Pattern recognition
Feature extraction
C382S225000, C382S274000
Reexamination Certificate
active
07085416
ABSTRACT:
Described herein is a process for objectively and automatically determining spectral endmembers and transforming Spectral Mixture Analysis (SMA) from a widely used research technique into a user-friendly tool that can support the needs of all types of remote sensing. The process extracts endmembers from a spectral dataset using a knowledge-based approach. The process identifies a series of starting spectra that are consistent with a scene and its environment. The process then finds endmembers iteratively, selecting each new endmember based on a combination of physically and statistically-based tests. The tests combine spectral and spatial criteria and decision trees to ensure that the resulting endmembers are physically representative of the scene.
REFERENCES:
patent: 6008492 (1999-12-01), Slater et al.
patent: 6075891 (2000-06-01), Burman
patent: 6608931 (2003-08-01), Sunshine et al.
patent: 6741740 (2004-05-01), Sunshine et al.
Yi-Hsing Tseng, Spectral Mixture Analysis of Hypersectral data, Nov. 1999, GIS Development, Asian Conference in Remote Sensing, 13 pages.
Gemmell et al, Estimating Forest Cover in a Boreal Forest Test using Thematic Mapper Data from Two Dates, Feb. 2001, Elsevier Science Inc., ISBN: 0034-4257, pp. 197-211.
ENVI Tutorial #10, Advanced Hyperspectral Analysis, 1995 AVIRIS, 18 Pages.
Preliminary Examination Report for Application No. PCT/US02/13497, dated Oct. 8, 2003 (mailing date).
International Search Report, dated Aug. 5, 2002.
Nirmal Keshava, John F. Mustard, “Spectral Unmixing,”IEEE Signal Processing Magazine, Jan., 2002, pp. 44-57.
John B. Adams, Milton O. Smith, Alan R. Gillespie, “Imaging Spectroscopy: Interpretation Based on Spectral Mixture Analysis,”Remote Geochemical Analysis, Oct. 2, 2002, pp. 145-166.
Examination of Applications, 700-93-700.95, Aug. 2001.
K. O. Niemann, David G. Goodenough, Andrew Dyk, A. S. Bhogal, University of Victoria, Pixel Unmixing for Hyperspectral Measurement of Foliar Chemistry in Pacific Northwest Coastal Forests, www.aft.pfc.forestry.ca/igarss99/Pixel—unmixing, Apr. 30, 2001.
Spectral Angle Mapper (SAM), www.phylab.mtu.edu/˜tip/remotesensing/tutorial/sam/sam, Apr. 30, 2001.
Manual Endmember Selection Tool, www.cires.Colorado.edu/cses/research/ems—man, Mar. 22, 2001.
Eugene W. Martin, “Measurement of Impervious Surface Area From Landsat Thematic Mapper Data Using Spectral Mixture Analysis,” www.commenspace.org/papers/sma, Mar. 22, 2001.
Gemmell, et al., “Estimating Forest Cover in a Boreal Forest Test Using Thematic Mapper Data from Two Dates,”Elsevier Science, Inc., ISBN: 0034-4257, pp. 197-211, Feb. 2001.
“Abacus,” www.saic.com/abacus, Feb. 26, 2001.
Sunshine, John F. Mustard, Melanie A. Harlow, Andrew Elmore, NASA Earth Science Enterprise Commercial Remote Sensing Program Affiliated Research Center Brown University, Dec. 17, 1999.
Yi-Hsing, Tseng, “Spectral Mixture Analysis of Hyperspectral Data,”GIS Development, Asian Conference in Remote Sensing, 13 pp., Nov. 1999.
John F. Mustard, Jessica M. Sunshine, “Spectral Analysis for Earth Science: Investigations Using Remote Sensing Data,”Remote Sensing for the Earth Sciences: Manual of Remote Sensing, 3 Ed., vol. 3, Copyright 1999, pp. 251-306.
ENVI Tutorial #10, “Advanced Hyperspectral Analysis,” 18 pp., 1995 AVIRIS.
J. B. Adams, D. E. Sabol, V. Kapos, R. A. Filho, D. A. Roberts, M. O. Smith, “Classification of Multispectral Images Based on Fractions of Endmembers: Application to Land-Cover Change in The Brazilian Amazon,”Remote Sens. Environment, 52:137-54, 1995.
J. B. Adams, Milton O. Smith, “Spectral Mixture Modeling: A New Analysis of Rock and Soil Types at the Viking Lander 1 Site,”Journal of Geophysical Research, vol. 91, No. 88, Jul. 10, 1986, pp. 8098-8112.
McNaron-Brown Kellie Sue
Sunshine Jessica Miriam
Tompkins Stefanie
Bali Vikkram
Science Applications International Corporation
LandOfFree
Method for selecting representative endmember components... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method for selecting representative endmember components..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method for selecting representative endmember components... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3659798