基于有约束CNMF的遥感高光谱多光谱图像融合

doi:10.3969/j.issn.0255-8297.2016.06.001

Abstract

Abstract:

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.

Key words: non-negative matrix factorization, hyperspectral image, sparseness constraint, spatial resolution, minimum volume constraint, image fusion

CLC Number:

TP751

LIU Yang, XU Hong-ping, YI Hang, SHI Qing-ping, XIA Wei-qiang, KANG Jian. Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF[J]. Journal of Applied Sciences, 2016, 34(6): 651-660.

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Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF

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