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L1 unmixing and its application to hyperspectral image enhancement

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.