N‐FindR method versus independent component analysis for lithological identification in hyperspectral imagery

References

• Adams , J. B. , Smith , M. O. and Johnson , P. E. 1986 . Spectral mixture analysis modelling: A new analysis of rock and soil types at the Viking Lander 1 site. . Journal of Geophysical Research , 91 : 8098 – 8112 .
   Web of Science ®Google Scholar
• Amari , A. , Cichocki , A. and Yang , H. H. 1996 . “ A new learning algorithm for blind signal separation. ” . In Advances in neural information processing systems , Edited by: Mozer , M , Touretzky , D and Hasselmo , M . 757 – 763 . Cambridge, MA : MIT Press . Vol. 8
   Google Scholar
• Bayliss , J. , Gualtieri , J. A. and Cromp , R. F. 1997 . “ Analysing hyperspectral data with independent component analysis. ” . In Applied Image and Pattern Recognition Workshop , 133 – 143 . Bellingham, WA : International Society for Optical Engineering (SPIE) . Vol. 3240
   Google Scholar
• Becker , T. , Hoffmann , K. and Schreiber , U. 1998 . “ Geologic map of Namibia, 1 : 250000 geological series sheet 2316 ‐ Rehoboth (provisional). ” . In Technical report , Windhoek, , Namibia : Ministry of Mines and Energy, Geological Survey of Namibia publication .
   Google Scholar
• Bell , A. J. and Sejnowsky , T. J. 1995 . An information‐maximization approach to blind separation and blind deconvolution. . Neural Computation , 7 : 1129 – 1159 .
   PubMed Web of Science ®Google Scholar
• Boardman , J. W. , Kruse , F. A. and Green , R. O. 1995 . “ Mapping target signatures via partial unmixing of AVIRIS data. ” . In Fifth JPL Airborne Earth Science Workshop , 23 – 26 . Pasadena, , CA : JPL Publication, 95‐1 . Vol. 1
   Google Scholar
• Carvalho , O. and Meneses , P. 2000 . “ Spectral correlation mapper (SCM): an improving spectral angle mapper. ” . In Ninth JPL Airborne Earth Science Workshop , 65 – 74 . Pasadena, , CA : JPL Publication, 0‐18 .
   Google Scholar
• Chang , C. I. , Chiang , S. S. , Smith , J. A. and Ginsberg , I. W. 2002 . Linear spectral random mixture analysis for hyperspectral imagery. . IEEE Transactions on Geosciences and Remote Sensing , 40 : 375 – 392 .
   Web of Science ®Google Scholar
• Clark , R. N. and Rousqh , T. L. 1984 . Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. . Journal of Geophysical Research , 89 : 6329 – 6340 .
   Web of Science ®Google Scholar
• Clark , R. N. , King , T. V. V. , Klejwa , M. and Swayze , G. A. 1990 . High spectral resolution spectroscopy of minerals. . Journal of Geophysical Research , 85 : 12, 653 – 12, 680 .
   Web of Science ®Google Scholar
• Comon , P. 1994 . Independent component analysis, a new concept? . Signal processing , 36 : 287 – 314 .
   Web of Science ®Google Scholar
• Du , Q. , Kopriva , I. and Szu , H. 2006 . Independent‐component analysis for hyperspectral remote sensing imagery classification. . Optical Engineering , 45 : 1 – 13 .
   Web of Science ®Google Scholar
• ENVI, Version 3.0 Tutorials, December, 1997
   Google Scholar
• Folkman , M. , Pearlman , J. , Liao , L. and Jarecke , P. 2001 . “ EO1/Hyperion hyperspectral imager design, development, characterization and calibration. ” . In Hyperspectral Remote Sensing of the Land and Atmosphere , 40 – 51 . Bellingham : SPIE Proc., International Society for Optical Engineering (SPIE) . Vol. 4151
   Google Scholar
• Gao , B. C. and Goetz , A. F. H. 1990 . Column atmospheric water vapor and vegetation liquid water retrievals from airborne imaging spectrometer data. . Journal of Geophysical Research , 95 : 3549 – 3564 .
   Web of Science ®Google Scholar
• Gao , B. , Heidebrecht , K. and Goetz , A. 1993 . Derivation of scaled surface reflectances from AVIRIS data. . Remote Sensing of Environment , 44 : 165 – 178 .
   Web of Science ®Google Scholar
• Green , A. A. , Berman , M. , Switzer , P. and Craig , M. D. 1988 . A transformation for ordering multispectral data in terms of image quality with implications for noise removal. . IEEE Transactions on Geoscience and Remote Sensing , 26 : 65 – 74 .
   Web of Science ®Google Scholar
• Heinz , D. and Chang , C. I. 2001 . Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery. . IEEE Transactions on Geoscience and Remote Sensing , 39 : 529 – 545 .
   Web of Science ®Google Scholar
• Hyvärinen , A. and Oja , E. 1997 . A fast fixed‐point algorithm for independent component analysis. . Neural Computation , 9 : 1483 – 1492 .
   Web of Science ®Google Scholar
• Hyvärinen , A. and Oja , E. 2000 . Independent component analysis: algorithms and applications. . Neural Networks , 13 : 411 – 430 .
   PubMed Web of Science ®Google Scholar
• Hyvärinen , A. , Karhunen , J. and Oja , E. 2001 . Independent Component Analysis , 481 Toronto : John Wiley and Sons .
   Google Scholar
• Hotelling , K. 1933 . Analysis of a complex of statistical variables into principal components. . Journal of Educational Psychology , 24 : 417 – 441 .
   Google Scholar
• Jutten , C. and Herault , J. 1991 . Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. . Signal Processing , 24 : 1 – 10 .
   Web of Science ®Google Scholar
• Kruse , F. , Lefkoff , A. , Boardman , J. , Heidebrecht , K. , Shapiro , A. , Barloon , P. and Goetz , A. 1993 . The spectral image processing system (SIPS): interactive visualization and analysis of imaging spectrometer data. . Remote Sensing of Environment , 44 : 145 – 163 .
   Web of Science ®Google Scholar
• Kruse , F. A. , Boardman , J. W. , Huntington , J. F. , Mason , P. and Quigley , M. A. Evaluation and Validation of EO‐1 Hyperion for Geologic Mapping. Proceeding IGARSS 2002 Conference , pp. 593 – 595 . Toronto, , Canada : IEEE 2002 International Publication .
   Google Scholar
• Kruse , F. A. 2003 . “ Mineral mapping with AVIRIS and EO‐1 Hyperion. ” . In Proceedings 12th JPL Airborne Geosciences Workshop 149 – 156 . JPL Publication
   Google Scholar
• Lee , J. B. , Woodyatt , S. and Berman , M. 1990 . Enhancement of high spectral resolution remote sensing data by a noise‐adjusted principal components transform. . IEEE Transactions on Geoscience and Remote Sensing , 28 : 295 – 304 .
   Web of Science ®Google Scholar
• Lennon , M. , Mercier , G. , Mouchot , M. C. and Hubert‐Moy , I. 2001 . “ Independent Component Analysis as a tool for the dimensionality reduction and the representation of hyperspectral images. ” . In Geoscience and Remote Sensing Symposium, IGARSS '01 , Sydney, , Australia : IEEE 2001 International Publication . 9–13 July 2001
   Google Scholar
• Nascimento , J. M. P. and Dias , J. M. B. 2005 . Does independent component analysis play a role in unmixing hyperspectral data? . GeoRS , 43 : 175 – 187 .
   Google Scholar
• Neville , R. A. , Staenz , K. , Szeredi , T. , Lefebvre , J. and Hauff , P. 1999 . “ Automatic End member Extraction From Hyperspectral Data for Mineral Exploration. ” . In Proceedings of the 21st Canadian Symposium on Remote Sensing 891 – 897 . 21–24 June 1999, Ontario, Canada, Vol. II
   Google Scholar
• Pham , D. T. , Garat , P. and Jutten , C. Separation of a mixture of independent sources through a maximum likelihood approach. Proceedings of EUSIPCO ′92, 6th European Signal Processing Conference , pp. 771 – 774 . Brussels, , Belgium : Elsevier .
   Google Scholar
• Robila , S. and Varshney , P. 2002 . “ Target detection in Hyperspectral images based on independent component analysis. ” . In Automatic Target Recognition XII , 173 – 182 . Bellingham : International Society for Optical Engineering (SPIE) . Washington, DC, Vol. 4726
   Google Scholar
• Shaw , G. and Burke , H. 2003 . Spectral imaging for remote sensing. . Lincoln Laboratory Journal , 14 : 3 – 28 .
   Google Scholar
• Tanre , D. , Deroo , C. , Duhaut , P. , Herman , M. , Morcrette , J. , Perbos , J. and Deschamps , P. 1986 . Simulation of Satellite Signal in the Solar Spectrum (6S): User's Guide, U.S.T. de Lille, Laboratoire d'Optique Atmosphérique, Lille
   Google Scholar
• Winter , E. M. 1999 . “ N‐FINDR: An algorithm for fast autonomous spectral end‐member determination in hyperspectral data. ” . In Imaging Spectrometry V , 266 – 277 . Bellingham : International Society for Optical Engineering (SPIE) . July 1999, Denver, Colorado, Proc. SPIE, Vol. 3753
   Google Scholar
• Zibulevsky , M. 2003 . “ Blind source separation with relative Newton Method. ” . In Proceedings of the Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA 2003) , 897 – 902 . Amsterdam : Elsevier Publisher .
   Google Scholar