基于回溯优化的非线性高光谱图像解混

Nonlinear unmixing using backtracking optimization for hyperspectral images

为了进一步提升高光谱图像的解混精度,提出一种基于回溯优化的高光谱图像后非线性解混算法。在后非线性混合模型的基础上,以观测图像与重构图像之间的重构误差为目标函数,使用回溯搜索算法在解空间搜索使目标函数取得极小值的最优解。在搜索过程中,利用回溯搜索算法的边界控制机制有效保证了高光谱图像解混过程中的约束条件,进而有效实现了对解混丰度值和非线性参数的精确估计。针对合成高光谱图像和真实高光谱遥感图像的解混实验表明,文中算法具有优异的解混性能,所达到的解混精度显著优于现有非线性高光谱图像解混算法。

A postnonlinear unmixing algorithm was presented for hyperspectral images based on backtracking optimization to improve the unmixing accuracy. On the basis of the postnonlinear mixing model, the reconstruction error between the observed images and the reconstructed images was used as the objective function, backtracking search optimization algorithm was used to search in the solution space to obtain the optimal solution which minimize the objective function. In the search process, the boundary control mechanism of the backtracking search optimization algorithm effectively ensured the constraint condition in the hyperspectral image unmixing, and then the abundance and nonlinear parameters can be estimated accurately. The experiments conducted for both synthetic images and real remote sensing images show that the algorithm proposed is provided with excellent unmixing performance. The unmixing accuracy achieved is significantly better than the state-of-the-art nonlinear hyperspectral images unmixing algorithms.