高光谱遥感图像的非线性光谱解混能弥补线性方法难以解释复杂场景中非线性混合效应的不足, 而双线性混合模型及算法是其研究的热点.提出了一种基于双线性混合模型几何特性的光谱解混算法.通过将模型中的非线性混合项表示为一个融合了共同非线性效应的额外端点的线性贡献, 使复杂的双线性混合模型求解转化为简单的线性解混问题.然后结合传统的线性解混算法直接迭代估计正确的丰度.模拟和真实遥感图像数据的实验结果表明, 与其它相关解混方法相比, 该算法能较好地克服共线性效应以及拟合优化过多参数对双线性混合模型求解造成的不利影响, 同时提高了解混的精度和速度.

Nonlinear spectral unmixing for hyperspectral remote sensing images can overcome the shortage of linear unmixing methods that failing in explaining the nonlinear mixing effect in more complex scenarios. Meanwhile, bilinear mixture models and their corresponding algorithms are the hot topic of related researches. A nonlinear spectral unmixing algorithm based on the geometric characteristics of bilinear mixture models was proposed. By representing the models’ nonlinear mixing terms as the linear contribution of one extra vertex concentrating the common nonlinear mixing effect, solving the complex bilinear mixture models was converted to do the simple linear spectral unmixing. Further, a traditional linear spectral unmixing algorithm was adopted to estimate the abundances directly in an iterative way. Experimental results on simulated and real hyperspectral images indicate that the proposed algorithm can overcome the collinearity effect and the adverse impact caused by fitting too many parameters, and improve both unmixing accuracy and computational speed.