Mineral mixtures are intimate mixtures have been extensively generated both on Earth and on other planets. The scatter of the particles that comprise mineral mixtures typically initiates complex optical interactions that are not interpreted by a linear mixing model. Moreover

the extraction of endmember signatures from the original data may be difficult given complex mineral mixtures and the lack of completely pure spectral signatures in the scene. Thus

we propose a sparse unmixing algorithm based on the spectral library of a Single Scattering Albedo（ SSA） to decompose intimate mixtures.The sparse unmixing method aims to determine the optimal subset of endmembers from a spectral library and to estimate its fractional abundances in each pixel. The data from the spectral library are considered prior information; thus

sparse unmixing does not rely on endmember extraction algorithms. The Hapke model can describe intimate mixing effects effectively

and SSA can be obtained based on the theory behind this model. The SSA of the component particles is a linear mixture; therefore

linear unmixing techniques can be applied in the SSA space instead of in the reflectance space. The data processing procedure in this study mainly consists of three steps:（ 1） Building the spectral library of reflectance and then resampling the library spectrum to wavelength range and position;（ 2） Converting the reflectance to SSA and constructing a SSA spectral library;（ 3） Sparse unmixing.The laboratory spectra of the mineral mixtures and of the AVIRIS Cuprite data set are used to verify our method. We can identify the endmembers of mixtures accurately given laboratory data

and the mean absolute retrieval error of abundance is 3. 12%.The qualitative analysis of AVIRIS hyperspectral data indicates that the abundance maps derived with our method are consistent with the Tricorder maps of United States Geological Survey（ USGS）

particularly in places where abundance is high; nonetheless

the abundance maps determined with our method still do not confirm to Tricorder maps to some extent. The classification maps of hyperspectral data consider each pixel to be pure

and each pixel is classified as the class of the representative endmember in the pixel. By contrast

unmixing classifies the scene at subpixel level

and the abundances represent the proportion of endmembers in a pixel.Experiment results show that our method can identify endmembers from the spectral library accurately

and can also estimate abundance well. The Hapke model can simply be used to minimize errors by calculating SSA directly. In our future work

we will calculate SSA exactly based on physical mineral parameters.