基于有约束CNMF的遥感高光谱多光谱图像融合

Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF

针对高光谱图像空间分辨率较低的问题,设计了一种基于光谱解混的高光谱、多光谱图像融合算法(VSC-CNMF).结合遥感图像的实际物理特性,在混合像元分解时加入端元单形体最小体积约束和丰度稀疏约束,通过光谱退化、空间退化和迭代解混,实现不同图像间端元和丰度的匹配,获得了具有高空间分辨率的融合图像.仿真实验表明,VSC-CNMF可得到具有更高空间质量和光谱质量的融合图像.

Hyperspectral images generally have lowspatial resolution due to limitations of the imaging spectrometer. In this paper, VSC-CNMF is designed to produce a fused image from hyperspectral and multi-spectral images. An end-member smallest volume and abundance sparseness constrained NMF （VSC-CNMF） algorithm is proposed based on the physical characteristics of remote sensing images. We match the end-member and abundance of two types of images by spectral and spatial degradations, and get the fused image with high spatial and spectral resolution according to some un-mixing update rules. Simulation results show that fused images with higher spatial and spectral quality can be obtained with VSC-CNMF.