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首页> 外文会议>International Conference on Automatic Control and Dynamic Optimization Techniques >Spectral unmixing based fusion algorithm for hyperspectral and multi-spectral images
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Spectral unmixing based fusion algorithm for hyperspectral and multi-spectral images

机译：基于光谱分解的高光谱和多光谱图像融合算法

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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：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）融合的目的是在光谱和空间域中获得高分辨率图像，这更适合于对象识别和精确的定位。提出了一种新颖的HSI和MSI融合算法，以获得更好的频谱保持性能。该方法从端元提取开始，以提取HSI中的纯对象光谱，然后使用通过光谱分解获得的这些对象的丰度对MSI中的像素进行分类，并确定这些像素属于哪个对象。一旦获得了端元提取和光谱解混结果，就可以通过将纯物体光谱分配给MSI像素来重建几乎没有光谱失真的融合HSI。实验结果表明，该算法为HSI和MSI的融合提供了一种有效的方法，并在最大程度上避免了频谱畸变。

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