CHEN Jin, MA Lei, CHEN Xuehong, et al. Research progress of spectral mixture analysis[J]. Journal of Remote Sensing, 2016,20(5):1102-1109. DOI: 10.11834/jrs.20166169.
Spectral Mixture Analysis(SMA) is one of the main topics in quantitative remote sensing research. It is able to provide land cover information at sub-pixel levels for practical applications. With the emergence of improved algorithms
SMA has made significant progress in many aspects
including spectral mixture models
endmember determination
endmember fraction inversion
and accuracy assessment. This study focused on these four key components in SMA and reviewed the available models and algorithms developed in last two decades. Moreover
the deficiencies of existing studies were analyzed. These deficiencies include the absences of widely accepted model selection criteria for linear and nonlinear spectral mixture analysis models and the unstable inversion of existing spectral mixture analysis caused by the high spectral correlation between endmembers. Finally
the study summarized the directions for future research
which include quantitatively evaluating the amplitude and spectral shape of multiple scattering among endmembers
identifying the factors that contribute to the nonlinear component in mixture observed signals by using radiative transfer models and laboratory measurement experiments
improving the robustness of linear spectral mixture analysis models
and suppressing high sensitivity to noise error signals resulting from collinearity with some insights from available statistical regression models for collinearity issues.