最小体积约束的高光谱图像分辨率增强算法

Hyperspectral Image Resolution Enhancement Algorithm with Minimum Volume Constraint

针对现有的高光谱多光谱图像融合算法解空间较大、未考虑高光谱数据的物理意义以及存在局部最优的问题,提出了一种基于单形体最小体积约束的耦合非负矩阵分解的高光谱与多光谱图像融合算法(MVC-CNMF)。该算法在混合像元解混的过程中,考虑图像的物理意义,加入了端元单形体最小体积约束。由仿真结果可以看出,该算法能有效地克服现有融合算法中的缺陷,实现了高光谱与多光谱图像的端元与丰度的精确匹配,获得高空间分辨率的融合图像,尤其适用于端元数目较多的高光谱图像。

The current hyperspectral and multi-spectral image fusion algorithms have such defects as having large solution space not considering the physical meaning of hyperspectral data and being prone to local optimal solutions. To solve these problems a hyperspectral and multi-spectral image fusion algorithm is proposed based on Minimum Volume Constraint and Coupled Non-negative Matrix Factorization( MVCCNMF). In the process of separating the mixed pixels the algorithm takes the physical meaning of the image into consideration and adds the minimum volume constraint of the endmember single body. Simulation results show that the proposed algorithm can effectively overcome the defects in the existing fusion algorithmsaccurately match the endmember with the abundance of hyperspectral and multi-spectral images and obtain high-spatial-resolution fused images. This algorithm is especially suitable for the hyperspectral images with a large number of endmembers.