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Nonnegative matrix factorization-based hyperspectral and panchromatic image fusion

机译:基于非负矩阵分解的高光谱和全色图像融合

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摘要

The fusion of hyperspectral image and panchromatic image is an effective process to obtain an image with both high spatial and spectral resolutions. However, the spectral property stored in the original hyperspectral image is often distorted when using the class of traditional fusion techniques. Therefore, in this paper, we show how explicitly incorporating the notion of "spectra preservation" to improve the spectral resolution of the fused image. First, a new fusion model, spectral preservation based on nonnegative matrix factorization (SPNMF), is developed. Additionally, a multiplicative algorithm aiming at get the numerical solution of the proposed model is presented. Finally, experiments using synthetic and real data demonstrate the SPNMF is a superior fusion technique for it could improve the spatial resolutions of hyperspectral images with their spectral properties reliably preserved.
机译:高光谱图像和全色图像的融合是获得具有高空间分辨率和光谱分辨率的图像的有效过程。但是,使用传统的融合技术时,原始高光谱图像中存储的光谱特性经常会失真。因此,在本文中,我们显示了如何显式结合“光谱保留”的概念以提高融合图像的光谱分辨率。首先,开发了一种新的融合模型,即基于非负矩阵分解(SPNMF)的光谱保留。此外,提出了一种针对该模型数值解的乘法算法。最后,使用合成和真实数据进行的实验表明,SPNMF是一种出色的融合技术,因为它可以提高高光谱图像的空间分辨率,并且可以可靠地保留其光谱特性。

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