针对高光谱图像像元中端元物质非线性混合的特点, 借鉴生物群智能现象, 提出一种基于双鸟群优化的高光谱图像非线性解混算法。为进一步提高非线性解混算法的精度, 通过模拟鸟群中觅食、警惕以及飞行等行为得到非线性问题的最优解。算法通过双鸟群的迭代优化来交替更新目标函数中的最优解以及非线性模型参数, 最终得到高光谱图像端元丰度的最佳估计。仿真实验和光谱数据实验结果表明: 双鸟群优化算法迭代收敛, 能克服局部最小值问题; 相比于同类算法, 该算法解混结果的丰度重建误差、平均光谱角距离和像元重建误差3项指标均较小, 该算法解混精度高, 像元重构效果好, 能有效提高高光谱图像非线性解混的精度。

Aiming at the characteristics of nonlinear mixing on end-member materials in hyperspectral image pixels, a nonlinear unmixing algorithm of hyperspectral images based on double-bird flock optimization was proposed referring to the phenomenon of biological swarm intelligence. In order to further improve the accuracy of nonlinear unmixing algorithm, the optimal solution of the nonlinear problem was obtained by simulating the behaviors of foraging, vigilance and flight in the bird flock. The algorithm alternately updated the optimal solution of the objective function and the nonlinear model parameters through iteration optimization of double-bird flocks, and finally obtained the optimal estimation of the end-member abundance on hyperspectral images. The experimental results of simulated data and actual spectral data show that the double-bird flock optimization algorithm iteration converges and can overcome the local minimum problem.Compared with similar algorithms, the three indices of abundance reconstruction error, average spectral angular distance and pixel reconstruction error of the algorithm are smaller, which shows that the proposed algorithm has high resolution of unmixing, good reconstruction effect of pixel, and can effectively improve the nonlinear unmixing accuracy of hyperspectral images.