An Adaptive Differential Evolution Endmember Extraction Algorithm for Hyperspectral Remote Sensing Imagery

In this letter, a new endmember extraction algorithm based on adaptive differential evolution (DE) (ADEE) is proposed for hyperspectral remote sensing imagery. In the proposed algorithm, the endmember extraction is transformed into a combinatorial optimization problem through constructing the objective function by minimizing the root mean square error between the original image and its remixed image. DE is utilized to search for the optimal endmember combination in the feasible solution space by the DE operators, such as crossover and mutation, which have the advantage of high efficiency, rapid convergence, and strong capability for global search. In addition, to avoid the problem of parameter selection, an adaptive strategy without user-defined parameters is utilized to improve the classical DE algorithm. The proposed method was tested and evaluated using both simulated and real hyperspectral remote sensing images, and the experimental results show that ADEE can obtain a higher extraction precision than the traditional endmember extraction algorithms.