基于约束非负矩阵分解的高光谱图像解混

Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization

光谱解混可以有效提升高光谱图像的利用效率。非负矩阵分解(NMF)常用于寻找非负数据的线性表示,可以有效解决混合像元问题。基于丰度的稀疏性和图像局部不变性提出一种高光谱解混算法。对丰度采取稀疏性约束和基于拉普拉斯矩阵的图正则项约束,构造了一个新的目标函数,端元和丰度在经过若干次迭代后取得了较好的解混合结果。该算法在模拟和真实数据上都进行了有效性验证,实验结果证明所提算法具有良好的解混性能。

Spectral unmixing can effectively improve the utilization efficiency of hyperspectral images.Nonnegative matrix factorization is frequently used to find linear representations of nonnegative data,which can effectively solve the problem of mixed pixels.A hyperspectral unmixing algorithm is proposed based on the sparsity of abundance and local invariance of an image.A new objective function is constructed by adopting the sparsity regularization term of abundance and the graph regularization term of the Laplacian matrix.Better unmixing results are obtained after several iterations of the endmembers and abundance.The proposed algorithm is validated using both simulation and real data,and the experimental results demonstrate that the proposed algorithm exhibits good performance.