Application of non-negative ICA for unmixing of Hyperion data
DOI:
https://doi.org/10.29150/jhrs.v4.5.p121-128Keywords:
hyperspectral, Hyperion, non-negative ICA, unmixingAbstract
Independent Component Analysis (ICA) is based on the linear mixture model of hyperspectral data and has been widely used in various blind source separations. Non-negative ICA considers non negativity constraint in addition to independency of components and the results will therefore be more real because negative signal sources in an image are not physically meaningful. The non-negative Independent Component Analysis (ICA) was used for detection and mapping of endmembers on a Hyperspectral dataset in this study. This paper surveys the capability of non-negative ICA for endmember detection and unmixing of Hyperion data in a geological terrain. In this research, a small part of the Hyperion scene was processed using the geodesic search algorithm to distinguish the Independent Components (ICs) of the image. The dataset was first topographically and atmospherically corrected and then data quality assessment was performed for recognizing the bad bands. The applied method for unmixing was able to detect the major minerals in the study scene and resulted in more accurate outcome in comparison to regular ICA. The non-negative ICA algorithm satisfactory identified three ICs, of which one showed considerable abundances in the region. The determined minerals are also more compatible to the geology and lithological features in the region. It therefore can map alterations and minerals abundances with higher accuracy. The abundances map of detected minerals was generated using spectral angle mapper method that proved as a useful tool for this purpose.References
Chang C.I., Du Q., 2004: Estimation of number of spectrally distinct signal source in hyperspectral imagery. – IEEE Transaction on Geoscience and Remote Sensing, 42: 608-619.
Du Q., Kopriva I., Szu H., 2006: Independent component analysis for hyperspectral remote sensing imagery classification. – Optical Engineering, 45(1): 017008. DOI:10.1117/1.2151172.
Edelman A., Arias T.A., Smith S.T., 1998: The geometry of algorithms with orthogonality constraints. – SIAM Journal on Matrix Analysis and Applications, 20: 303-353.
Harsanyi J.C., Farrand W., Chang C.I., 1993: Determining the number and identity of spectral endmembers: an integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization. – Proc. of 9th Thematic Conference of Geologic Remote Sensing, California, USA., pp. 395-408.
Hyvarinen A., Karhunen J., Oja E., 2001: Independent Component Analysis. – John Wiley & Sons, pp. 247.
Keshava N., Mustard J., 2002: Spectral unmixing. IEEE Signal Processing Mag, 19: 44-57.
Manolakis D., Marden D., Shaw G.A., 2003: Hyperspectral image processing for automatic target detection application. – Lincoln Laboratory Journal, 14: 79-166.
Martinez P., Perez R.M., Plaza A., Aguilar PL., Cantero M.C., Plaza J., 2006: Endmember extraction algorithms from hyperspectral images. – Annals of Geophysics, 49(1): 93-101.
Mehrpartov M., 1997: 1:100000 Geological Map and geological report of Siahrood. – Geological Survey of Iran.
Oskouei M.M., 2010: Independent component analysis of Hyperion data to map alteration zones. – Journal of photogrammetry, remote sensing and geoinformation processing (PFG), 2010/3: 179-189. DOI: 10.1127/1432-8364/2010/0048.
Plumbley M., 2003: Algorithm for non-Negative Independent Component Analysis. – IEEE Transactions on Neural Networks, 14: 534 – 543.
Riaño D., Chuvieco E., Salas J., Aguado I., 2003: Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. – IEEE Transactions on Geoscience and Remote sensing, 41(5): 1056-1061.
Soenen S.A., Peddle D.R., Coburn C.A., 2005: A modified sun-canopy-sensor topographic correction in forested terrain. – IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2148-2159.
Wang J., Chang C.I., 2006: Independent component analysis with application in hyperspectral image analysis. – IEEE Trasaction on Geoscience and Remote Sensing, 44: 1586-1600.
Wang J., Chang CI., 2006: Applications of Independent Component Analysis (ICA) in endmember extraction and abundance quantification for hyperspectral imagery. – IEEE Transactions on Geoscience & Remote Sensing, 44(9): 2601-2616
Zheng C.H., Huang D.S., Sun Z.L., Lyu M.R., Lok T.M., 2006: Nonnegative independent component analysis based on minimizing mutal information technique. – Neuro Computting, 69: 878-883.
Du Q., Kopriva I., Szu H., 2006: Independent component analysis for hyperspectral remote sensing imagery classification. – Optical Engineering, 45(1): 017008. DOI:10.1117/1.2151172.
Edelman A., Arias T.A., Smith S.T., 1998: The geometry of algorithms with orthogonality constraints. – SIAM Journal on Matrix Analysis and Applications, 20: 303-353.
Harsanyi J.C., Farrand W., Chang C.I., 1993: Determining the number and identity of spectral endmembers: an integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization. – Proc. of 9th Thematic Conference of Geologic Remote Sensing, California, USA., pp. 395-408.
Hyvarinen A., Karhunen J., Oja E., 2001: Independent Component Analysis. – John Wiley & Sons, pp. 247.
Keshava N., Mustard J., 2002: Spectral unmixing. IEEE Signal Processing Mag, 19: 44-57.
Manolakis D., Marden D., Shaw G.A., 2003: Hyperspectral image processing for automatic target detection application. – Lincoln Laboratory Journal, 14: 79-166.
Martinez P., Perez R.M., Plaza A., Aguilar PL., Cantero M.C., Plaza J., 2006: Endmember extraction algorithms from hyperspectral images. – Annals of Geophysics, 49(1): 93-101.
Mehrpartov M., 1997: 1:100000 Geological Map and geological report of Siahrood. – Geological Survey of Iran.
Oskouei M.M., 2010: Independent component analysis of Hyperion data to map alteration zones. – Journal of photogrammetry, remote sensing and geoinformation processing (PFG), 2010/3: 179-189. DOI: 10.1127/1432-8364/2010/0048.
Plumbley M., 2003: Algorithm for non-Negative Independent Component Analysis. – IEEE Transactions on Neural Networks, 14: 534 – 543.
Riaño D., Chuvieco E., Salas J., Aguado I., 2003: Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. – IEEE Transactions on Geoscience and Remote sensing, 41(5): 1056-1061.
Soenen S.A., Peddle D.R., Coburn C.A., 2005: A modified sun-canopy-sensor topographic correction in forested terrain. – IEEE Transactions on Geoscience and Remote Sensing, 43(9): 2148-2159.
Wang J., Chang C.I., 2006: Independent component analysis with application in hyperspectral image analysis. – IEEE Trasaction on Geoscience and Remote Sensing, 44: 1586-1600.
Wang J., Chang CI., 2006: Applications of Independent Component Analysis (ICA) in endmember extraction and abundance quantification for hyperspectral imagery. – IEEE Transactions on Geoscience & Remote Sensing, 44(9): 2601-2616
Zheng C.H., Huang D.S., Sun Z.L., Lyu M.R., Lok T.M., 2006: Nonnegative independent component analysis based on minimizing mutal information technique. – Neuro Computting, 69: 878-883.
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Published
2014-12-11
How to Cite
Oskouei, M. M., & Poormirzaee, R. (2014). Application of non-negative ICA for unmixing of Hyperion data. Journal of Hyperspectral Remote Sensing, 4(5), 121–128. https://doi.org/10.29150/jhrs.v4.5.p121-128
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Hyperspectral remote sensing and Atmosphere
