Debba, Pravesh2009-08-282009-08-282009Debba, P. 2009. Spectral unmixing: estimating partial abundances. Logistics and Quantitative Methods (LQM) Seminar Presentation 2009, Built Environment, CSIR. pp 1-22http://hdl.handle.net/10204/3549This presentation was presented at the Logistics and Quantitative Methods (LQM Seminar at the CSIRMost spectral unmixing techniques are variants of algorithms involving matrix inversion. Major problem in spectral unmixing is the non-orthogonality of end-members. The ability to estimate abundances in complex mixtures through spectral unmixing techniques is complicated when considering very similar spectral signatures. Iron-bearing oxide/hydroxide/sulfate minerals have similar spectral signatures. The study focuses on how could estimates of abundances of spectrally similar iron-bearing oxide/hydroxide/sulfate minerals in complex mixtures be obtained using hyperspectral data?enSpectral unmixingEnd-member spectraSynthetic mixturesSpectral reflectance vectorComputed abundance vectorLinear spectral mixture analysisLSMAMineralsBuilt Environment CSIRConference PresentationDebba, P. (2009). Spectral unmixing: estimating partial abundances. http://hdl.handle.net/10204/3549Debba, Pravesh. "Spectral unmixing: estimating partial abundances." (2009): http://hdl.handle.net/10204/3549Debba P, Spectral unmixing: estimating partial abundances; 2009. http://hdl.handle.net/10204/3549 .TY - Conference Presentation AU - Debba, Pravesh AB - Most spectral unmixing techniques are variants of algorithms involving matrix inversion. Major problem in spectral unmixing is the non-orthogonality of end-members. The ability to estimate abundances in complex mixtures through spectral unmixing techniques is complicated when considering very similar spectral signatures. Iron-bearing oxide/hydroxide/sulfate minerals have similar spectral signatures. The study focuses on how could estimates of abundances of spectrally similar iron-bearing oxide/hydroxide/sulfate minerals in complex mixtures be obtained using hyperspectral data? DA - 2009 DB - ResearchSpace DP - CSIR KW - Spectral unmixing KW - End-member spectra KW - Synthetic mixtures KW - Spectral reflectance vector KW - Computed abundance vector KW - Linear spectral mixture analysis KW - LSMA KW - Minerals KW - Built Environment CSIR LK - https://researchspace.csir.co.za PY - 2009 T1 - Spectral unmixing: estimating partial abundances TI - Spectral unmixing: estimating partial abundances UR - http://hdl.handle.net/10204/3549 ER -