基于神经网络和差分搜索的高光谱图像非线性解混

Nonlinear hyperspectral unmixing algorithm based on neural network and differential search

针对基于双线性混合模型(BMM)的高光谱图像梯度解混算法的局限性,提出一种基于神经网络(NN)和差分搜索算法(DSA)的非线性高光谱图像解混算法。在考虑p阶多项式模型的基础上,利用NN估计出实际高光谱图像的非线性阶数。构造解混的目标函数,将非线性解混问题转化为最优化问题。引入DSA对目标函数进行优化,将解混过程中的待求参数映射为差分搜索过程中的位置参数,同时在搜索过程中引入丰度非负和全加性约束映射机制满足解混要求。仿真数据和实际高光谱数据实验结果表明,本文算法有效地克服了基于BMM的梯度解混算法的不足,可有效实现高光谱图像的非线性解混。当NN采用2 000个样本训练,解混真实高光谱数据得到相应的重构误差(RE)达到1.15×10-2,具有良好解混效果。

Due to the existing limitations of hyperspectral image gradient descen t unmixing algorithm based on bilinear mixing model,a novel nonlinear unmixing algorithm based on the neural network （NN） and differential search algorithm （DSA） is proposed.Based on the p-order polynomial model,the NN can be used to efficiently estimate the nonlinearity order of real hyperspectral data pixels.We construct the objective function in order to turn the nonlinear unmixing problem into an optimization problem for hyperspectral da ta unmixing.By introducing the DSA,the proposed algorithm transforms the parameters to be solved in the hyperspectral unmixing problem into the position parameters in the search process of DSA.And the mapping mechan ism is introduced to meet the requirements of the abundance nonnegative constraint and abundance sum-to-one constraint.The expe rimental results show that the proposed framework has promising performances com pared with gradient nonlinear unmixing algorithms.When using 2000samples to train the NN and using the proposed algorithm to unmix the real hyperspectral data,the reconstruction error （RE） of unmixing index can reach about 1.15×10-2 which is better compared with t hose of other existing hyperspectral gradient descent nonlinear un mixing algorithms.