Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1963
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dc.contributor.authorallMartínez, P. J.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.authorallPérez, R. M.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.authorallPlaza, A.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.authorallAguilar, P. L.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.authorallCantero, M. C.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.authorallPlaza, J.; Computer Science Department, University of Extremadura, Cáceres, Spainen
dc.date.accessioned2006-12-07T14:39:01Zen
dc.date.available2006-12-07T14:39:01Zen
dc.date.issued2006-02en
dc.identifier.urihttp://hdl.handle.net/2122/1963en
dc.description.abstractDuring the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. Some of these sensors are already available on space-borne devices. Space-borne sensors are currently acquiring a continual stream of hyperspectral data, and new efficient unsupervised algorithms are required to analyze the great amount of data produced by these instruments. The identification of image endmembers is a crucial task in hyperspectral data exploitation. Once the individual endmembers have been identified, several methods can be used to map their spatial distribution, associations and abundances. This paper reviews the Pixel Purity Index (PPI), N-FINDR and Automatic Morphological Endmember Extraction (AMEE) algorithms developed to accomplish the task of finding appropriate image endmembers by applying them to real hyperspectral data. In order to compare the performance of these methods a metric based on the Root Mean Square Error (RMSE) between the estimated and reference abundance maps is used.en
dc.format.extent1038958 bytesen
dc.format.mimetypeapplication/pdfen
dc.language.isoEnglishen
dc.relation.ispartofseries1/49 (2006)en
dc.subjectendmember extractionen
dc.subjecthyperspectral mixturesen
dc.subjectlinear spectral unmixingen
dc.titleEndmember extraction algorithms from hyperspectral imagesen
dc.typearticleen
dc.type.QualityControlPeer-revieweden
dc.subject.INGV05. General::05.05. Mathematical geophysics::05.05.99. General or miscellaneousen
dc.relation.referencesBATESON, C.A., G.P. ASNER and C.A. WESSMAN (2000): Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis, IEEE Trans. Geosci. Remote Sensing, 38 (2), 1083- 1094. BOARDMAN, J.W (1998): Post-ATREM polishing of AVIRIS apparent reflectance data using EFFORT: a lesson in accuracy versus precision, in Proceedings of the VII NASA/JPL Airborne Earth Science Workshop. BOARDMAN, J.W., F.A. KRUSE and R.O. GREEN (1995): Mapping Target Signatures via Partial Unmixing of AVIRIS Data, in Summaries of the V JPL Airborne Earth Science Workshop. CLARK, R.N. (1999): Spectroscopy of rocks and minerals and principles of spectroscopy, in Principles of Spectroscopy, Manual of Remote Sensing, edited by A.N. RENCZ (John Wiley and Sons, New York), ch. 1, 3-58. GAO, B.-C., K.B. HEIDEBRECHT and A.F.H. GOETZ (1993): Derivation of scaled surface reflectances from AVIRIS data, Remote Sensing Environ., 44, 145-163. GREEN, R.O., M.L. EASTWOOD, C.M. SARTURE, T.G. CHRIEN, M. ARONSSON, B.J. CHIPPENDALE, J.A. FAUST, B.E. PAVRI, C.J. CHOUIT, M. SOLIS, M.R. OLAH and O. WILLIAMS (1998): Imaging Spectroscopy and the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), Remote Sensing Environ, 65 (3), 227-248. HEINZ, D. and C.-I. CHANG (2000): Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery, IEEE Trans. Geosci. Remote Sensing, 39, 529-545. IFARRAGUERRI, A. and C.-I. CHANG (1999): Multispectral and hyperspectral image analysis with convex cones, IEEE Trans. Geosci. Remote Sensing, 37 (2), 756- 770. LAMBERT, P. and J. CHANUSSOT (2000): Extending mathematical morphology to color image processing, in First Int. Conf. on Color in Graphics and Image Processing (CGIP’2000), October 2000, Saint-Etienne, France, 158-163. MADHOK, V. and D. LANDGREBE (1999): Spectral-spatial analysis of remote sensing data: an image model and a procedural design, Ph.D. Thesis; and School of Electrical & Computer Engineering Technical Report TRECE 99-10. PLAZA, A., P. MARTÍNEZ, R.M. PÉREZ and J. PLAZA (2002): Spatial/spectral endmember extraction by multi-dimensional morphological operations, IEEE Trans. Geosci. Remote Sensing, 40 (9), 2025-2041. SERRA, J. (1982): Image Analysis and Mathematical Morphology (Academic Press, London). SERRA, J. (1993): Image Analysis and Mathematical Morphology (Academic Press, London), vol. 1. STERNBERG, S.R. (2000): Greyscale Morphology, Computer Vision Graphics and Image Processing, 35, 283-305. THEILER, J., D.D. LAVENIER, N.R. HARVEY, S.J. PERKINS and J.J. SZYMANSKI (2000): Using blocks of skewers for faster computation of Pixel Purity Index, SPIE Proc., 4132, 61-71. WINTER, M.E. (1999): N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data, SPIE Proc., 3753, 266-275.en
dc.description.journalTypeJCR Journalen
dc.description.fulltextopenen
dc.contributor.authorMartínez, P. J.en
dc.contributor.authorPérez, R. M.en
dc.contributor.authorPlaza, A.en
dc.contributor.authorAguilar, P. L.en
dc.contributor.authorCantero, M. C.en
dc.contributor.authorPlaza, J.en
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
dc.contributor.departmentComputer Science Department, University of Extremadura, Cáceres, Spainen
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.grantfulltextopen-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.author.deptComputer Science Department, University of Extremadura, Cáceres, Spain-
crisitem.classification.parent05. General-
Appears in Collections:Annals of Geophysics
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