Characterizing dark spectra in mercury surface observations by nonlinear hyperspectral modeling

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

Volumn 2015-June, Issue , 2015, Pages

  Marinoni, Andrea ^a   Klima, Rachel ^b   Gamba, Paolo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; SPACE OPTICS; SPECTROSCOPY; SPECTRUM ANALYSIS;

EFFICIENT RECONSTRUCTION; HYPERSPECTRAL MODELS; MULTI-DIMENSIONAL SPACE; NONLINEAR COMBINATION; REGULAR OSCILLATIONS; SPECTRAL ANALYSIS METHOD; SPECTRAL SIGNATURE; THERMAL ENVIRONMENT;

REMOTE SENSING;

EID: 85039170579     PISSN: 21586276     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WHISPERS.2015.8075451     Document Type: Conference Paper

References (9)

1. R. Klima, N. Izenberg, G. Holsclaw, J. Helbert, M. D'Amore, W. McClintock, S. Solomon. Visible to near-infrared hyperspectral measurements of mercury: Challenges for deciphering surface mineralogy. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, June 2014.
2. R. Heylen, M. Parente, P. Gader. A review of nonlinear hyperspectral unmixing methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6):1844-1868, June 2014.
3. A.Marinoni and P. Gamba. Non-linear hyperspectral unmixing by polytope decomposition. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, June 2014.
4. A. Marinoni and P. Gamba. Order-nonlinear hyperspectral unmixing by sinusoidal polytope decomposition. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, June 2015.
5. J.M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot. Hyperspectral unmixing overview: Geometrical, statistical, sparse regression-based approaches. IEEE J. Select. Topics Appl. Earth Observ. Remote Sensing, 5(2):354-379, April 2012.
6. G.H. Golub and C. F. Van Loan. Matrix computations. Baltimore, MD: The Johns Hopkins University Press, 3nd edition, 1996.
7. V. Kaibel and A. Schwartz. On the complexity of polytope isomorphism problems. Graphs and combinatorics, 19(2):215-230, 2003.
8. G.E. Bredon. Topology and geometry. Springer Verlag, 1993.
9. H. Ren and C.-I Chang. Automatic spectral target recognition in hyperspectral imagery. IEEE Trans. Aerospace and Electron. Syst., 39(4):1232-1249, 2003.