Nonlinear Spectral Unmixing With a Linear Mixture of Intimate Mixtures Model

We present a new model for nonlinear spectral mixing observed in hyperspectral imagery and demonstrate how this model can be used for unmixing and obtaining abundance maps. The model is based on the idea that a single pixel can contain several spatially segregated areas containing different mineral mixtures and fuses Hapke's radiative transfer model for intimate mineral mixtures with the traditional linear mixing model. The resulting model allows great flexibility for generating spectra, provides abundance coefficients in terms of total relative ground cover for each endmember, and can be reduced to several other nonlinear mixing models by an appropriate choice of the parameters. Experiments on laboratory mineral mixtures and real hyperspectral imagery show reduced reconstruction errors and more accurate abundance coefficients compared with the linear mixing model or the recently introduced multimixture pixel model. Moreover, the reconstruction error improvement can be used as a per-pixel measure of the size of the intimate mixing component.

[1]  G. Rybicki Radiative transfer , 2019, Climate Change and Terrestrial Ecosystem Modeling.

[2]  John F. Mustard,et al.  Abundance and distribution of ultramafic microbreccia in Moses Rock dike - Quantitative application of mapping spectroscopy , 1987 .

[3]  Carle M. Pieters,et al.  Effects of grain size and shape in modeling reflectance spectra of mineral mixtures , 1991 .

[4]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Paul E. Johnson,et al.  A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures , 1983 .

[6]  J. Freud Theory Of Reflectance And Emittance Spectroscopy , 2016 .

[7]  John R. Miller,et al.  Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated‐forest hyperspectral data , 2009 .

[8]  Amit Banerjee,et al.  A comparison of kernel functions for intimate mixture models , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[9]  Antonio J. Plaza,et al.  Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  B. Hapke Bidirectional reflectance spectroscopy: 1. Theory , 1981 .

[11]  Jean-Yves Tourneret,et al.  Nonlinear unmixing of hyperspectral images using a generalized bilinear model , 2011 .

[12]  John F. Mustard,et al.  Spectral unmixing , 2002, IEEE Signal Process. Mag..

[13]  José M. Bioucas-Dias,et al.  Unmixing hyperspectral intimate mixtures , 2010, Remote Sensing.

[14]  José M. Bioucas-Dias,et al.  Nonlinear mixture model for hyperspectral unmixing , 2009, Remote Sensing.

[15]  Joseph N. Wilson,et al.  Using physics-based macroscopic and microscopic mixture models for hyperspectral pixel unmixing , 2012, Defense + Commercial Sensing.