L1 unmixing and its application to hyperspectral image enhancement
L1 unmixing and its application to hyperspectral image enhancement
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DOI：
10.1117/12.818245
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发表时间：
2009-04
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通讯作者：
Zhaohui Guo;Todd Wittman;S. Osher
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文献类型：
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作者：
Zhaohui Guo;Todd Wittman;S. Osher
由于高光谱图像通常分辨率较低，因此图像中的一个像素可能包含几种材料。确定单个像素中代表性物质的丰度的过程称为光谱解混。讨论了基于Bregman迭代的L1解混模型和快速计算方法。然后，我们使用解混信息和总变化（TV）最小化来产生更高分辨率的高光谱图像，其中每个像素都被驱动到“纯”材料。该方法产生的图像具有更高的视觉质量，可用于指示特征的亚像素位置。
Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the unmixing information and Total Variation (TV) minimization to produce a higher resolution hyperspectral image in which each pixel is driven towards a "pure" material. This method produces images with higher visual quality and can be used to indicate the subpixel location of features.