首页> 外文会议>2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing >Hyperspectral, multispectral, and panchromatic data fusion based on coupled non-negative matrix factorization
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Hyperspectral, multispectral, and panchromatic data fusion based on coupled non-negative matrix factorization

机译:基于非负矩阵分解的高光谱,多光谱和全色数据融合

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Coupled non-negative matrix factorization (CNMF) is applied to hyperspectral, multispectral, and panchromatic data fusion. This unmixing based method extracts and fuses hyperspectral endmember spectra and high-spatial-resolution abundance maps using these three data. An experiment with the synthetic data simulating ALOS-3 (advanced land observing satellite 3) dataset shows that the CNMF method has a possibility to produce fused data which have both high spatial and spectral resolutions with smaller spectral distortion.
机译:耦合非负矩阵分解(CNMF)被应用于高光谱,多光谱和全色数据融合。这种基于混合的方法使用这三个数据提取并融合了高光谱最终成员光谱和高空间分辨率的丰度图。用模拟ALOS-3(高级陆地观测卫星3)数据集的合成数据进行的实验表明,CNMF方法有可能产生融合的数据,这些数据既具有较高的空间分辨率又具有较高的光谱分辨率,且光谱失真较小。

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