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Improving multisource image fusion using thematic content

机译:使用主题内容改进多源图像融合

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This study investigates the use of thematic class correspondence in the fusion of hyperspectral data with higher spatial resolution synthetic aperture radar (SAR) data. A thematic map derived from the SAR imagery is used to introduce spatial information into the hyperspectral imagery, a spatial-spectral fusion. Because the underlying physical processes measured by the imaging systems substantially differ, classes derived from one may have partial or no relationship to classes from the other. In our approach, SAR-derived class contributions to a mixed hyperspectral pixel are weighted in the fusion process based on their correspondence with spectral classes. Unconstrained and weighted least squares solutions for the resulting linear system are described. A comparison of fusion results is presented with and without use of thematic content.
机译:本研究调查了使用较高空间分辨率合成孔径雷达(SAR)数据的高光谱数据融合中的主题类对应。源自SAR图像的主题映射用于将空间信息引入高光谱图像,空间光谱融合。因为成像系统测量的底层物理处理基本上不同,所以从一个源自衍生的类可能与来自另一个的类别局部或没有关系。在我们的方法中,基于与光谱类的对应关系,在融合过程中加权对混合高光谱像素的SAR衍生的类贡献。描述了用于所得到的线性系统的无约束和加权最小二乘解。融合结果的比较呈现出和不使用主题内容。

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