基于高阶非线性模型的多目标高光谱图像解混算法

Multi-objective hyperspectral unmixing algorithm based on high-order nonlinear mixing model

在高阶非线性混合模型的基础上,提出一种多目标高光谱图像解混算法,解决传统方法受高光谱数据异常值影响而解混精度不高的问题。该算法以重构误差与光谱角分布为目标函数建立优化模型,并同时优化两目标函数以减少数据异常值对模型求解的影响,使解混结果在两个评价指标上得到提升;最后采用差分搜索算法求解多目标优化模型,解决梯度类优化方法易陷入局部极值的问题,从而进一步提升解混精度。实验结果表明,文中算法与传统高光谱解混算法相比,具有更精确的端元丰度估计结果和更高的解混精度。

Based on high-order nonlinear mixing model, a multi-objective hyperspectral unmixing algorithm was proposed, which solved the problem that the traditional method cannot obtain higher unmixing accuracy due to the outliers of hyperspectral image data. The proposed algorithm took the reconstruction error and spectral angle mapper as the objective functions and optimized them in order to reduce the outliers influence of hyperspectral data on the solution of optimization model and improve the two evaluation indicators. Then, the difference search algorithm was used to solve the multi-objective optimization model and overcame the tendency of the traditional gradient-based optimization method to fall into the local extremum problem and further improved the unmixing accuracy. The experiment results show that the proposed algorithm has more accurate endmembers abundance estimation and higher unmixing accuracy.