基于光谱和空间特性的高光谱解混方法

摘要

为表征高光谱数据的光谱和空间特性,引入光谱的平滑性和地物空间分布的稀疏性约束,提出非负矩阵分解的改进算法,将其应用于高光谱解混.尺度可变的梯度下降算法保证了改进算法的收敛性.实验结果表明,改进后的非负矩阵分解算法能给出地物光谱,并精确估计其分布.%To represent the spectral and spatial character of hyperspectral data,by introducing the smoothness constraint of hyperspectral data and the sparseness constraint of spatial distribution of the materials,an improved nonnegative matrix factorization(INMF)was used for hyperspectral unmixing.Its monotonic convergence is guaranteed by using a gradient-based optimization algorithm.Experiments demonstrate that the INMF algorithm is yielding accurate estimation of both endmember spectra and abundance maps.