高光谱图像解混是高光谱数据分析的重要研究内容.在现有混合模型的基础上, 提出一种新的高光谱图像非线性解混算法.通过在目标函数中引入丰度的非负及和为一约束以及非线性参数的有界约束, 该算法将高光谱图像非线性解混问题转化为求解丰度矢量和非线性参数的约束非线性最小二乘问题, 继而采用一种交替迭代优化算法求解该问题.仿真和实际高光谱数据的实验结果表明, 所提出的算法有效地克服了线性解混的不足, 同时具有良好的抗噪声性能, 可以作为一种解决高光谱遥感图像非线性解混的有效手段.

Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, a new nonlinear unmixing algorithm for hyperspectral imagery was proposed. By introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters, the proposed algorithm transforms the hyperspectral unmixing problem into a constrained nonlinear least squares problem. It consists of two sub-problems which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. Then, the alternating iterative optimization technique was used to solve this problem. The experimental results on synthetic and real hyperspectral dataset demonstrated that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.