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Spectral unmixing based fusion algorithm for hyperspectral and multi-spectral images

机译：基于光谱的超光谱和多光谱图像的基于融合算法

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The intention for fusion of low-spatial-resolution hyperspectral image (HSI) and high-spatial-resolution multi-spectral image (MSI) is to obtain a high-resolution image in both spectral and spatial domains, which is more suitable for object identification and precise positioning. A novel fusion algorithm for HSI and MSI is proposed in this paper to obtain a better spectral preserving performance. The method commences by endmembers extraction to extract the pure object spectra in HSI, then the abundances of these objects obtained by spectral unmixing are used to classify the pixels in MSI and determine which object these pixels belong to. Once the endmembers extraction and spectral unmixing results are obtained, a fused HSI with almost no distortion in spectra will be reconstructed by assigning the pure object spectra to pixels of MSI. The experimental results demonstrate that the proposed algorithm provides an effective way for fusion of HSI and MSI and avoids the spectral distortion to the most extent.
机译：融合低空间分辨率高光谱图像（HSI）和高空间分辨率多光谱图像（MSI）的意图是在光谱和空间域中获得高分辨率图像，其更适合对象识别精确定位。本文提出了一种用于HSI和MSI的新型融合算法，以获得更好的谱保持性能。该方法由endMembers提取开始以提取HSI中的纯对象谱，然后通过光谱解密而获得的这些对象的丰度用于对MSI中的像素进行分类，并确定这些像素属于哪个对象。一旦获得了终端用因子和光谱解密结果，将通过将纯对象光谱分配给MSI的像素来重建熔点中几乎没有失真的融合HSI。实验结果表明，所提出的算法为HSI和MSI融合提供了有效的方法，并避免了大多数程度的光谱失真。

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