Parallel Implementation of the Multiple Endmember Spectral Mixture Analysis Algorithm for Hyperspectral Unmixing

In the last decade, the issue of endmember variability has received considerable attention, particularly when each pixel is modeled as a linear combination of endmembers or pure materials. As a result, several models and algorithms have been developed for considering the effect of endmember variability in spectral unmixing and possibly include multiple endmembers in the spectral unmixing stage. One of the most popular approach for this purpose is the multiple endmember spectral mixture analysis (MESMA) algorithm. The procedure executed by MESMA can be summarized as follows: (i) First, a standard linear spectral unmixing (LSU) or fully constrained linear spectral unmixing (FCLSU) algorithm is run in an iterative fashion; (ii) Then, we use different endmember combinations, randomly selected from a spectral library, to decompose each mixed pixel; (iii) Finally, the model with the best fit, i.e., with the lowest root mean square error (RMSE) in the reconstruction of the original pixel, is adopted. However, this procedure can be computationally very expensive due to the fact that several endmember combinations need to be tested and several abundance estimation steps need to be conducted, a fact that compromises the use of MESMA in applications under real-time constraints. In this paper we develop (for the first time in the literature) an efficient implementation of MESMA on different platforms using OpenCL, an open standard for parallel programing on heterogeneous systems. Our experiments have been conducted using a simulated data set and the cIMAGMA mathematical library. This kind of implementations with the same descriptive language on different architectures are very important in order to actually calibrate the possibility of using heterogeneous platforms for efficient hyperspectral imaging processing in real remote sensing missions.