Spectral unmixing is a key hyperspectral remote sensing image processing technique. Many spectral unmixing algorithms have been proposed. Most of these algorithms are based on the assumption that pure pixels exist in the hyperspectral imagery. However

when pure pixels are lacking

the performance of these algorithms may deteriorate. The simplex volume minimization (VolMin) method provides a good means to overcome this problem. However

VolMin is a complex constraint optimization problem. Owing to the uncertainty that noise exists in an image

the algorithm is easily trapped into local optima. Thus

we introduce a swarm intelligence technique

i.e.

differential evolution (DE) algorithm

into the VolMin procedure. By utilizing the powerful global searching capability and high-dimensional adaptability of DE

we develop a minimum simplex volume DE (VolMin-DE) spectral unmixing algorithm through VolMin-DE encoding. Synthetic mixture and real image data are utilized for comparative experiments on unmixing accuracy. The proposed VolMin-DE algorithm

vertex component analysis

minimum distance constrained non-negative matrix factorization (NMF)

and minimum volume constrained NMF are compared. Experimental results indicate that the proposed VolMin-DE algorithm outperforms the other algorithms. The precision (spectral angle distance) of VolMin-DE can be increased by 7.8%

especially when the accuracy of 10 endmembers is improved by 41.3%. In the noise range of 20 dB to 50 dB

the performance of VolMin-DE varies from 1.9 to 3.2 (unit: degrees)

whereas that of the traditional algorithm varies from 2.2 to 3.5. This result demonstrates that the proposed algorithm has better noise robustness than other algorithms. The proposed VolMin-DE algorithm can be applied to hyperspectral imagery regardless of the pure pixel assumption (maximum purity level equal to or greater than 0.8 is recommended). The algorithm does not require dimensional reduction;hence

error accumulation is avoided. VolMin-DE has good potential applications in hyperspectral unmixing.