Unmixing with Subspace Method and Application to Hyper Spectral Image.
نویسندگان
چکیده
منابع مشابه
Unmixing and target recognition in hyper - spectral
4 We present two new linear algorithms that perform unmixing in hyper-spectral 5 images and then recognize their targets whose spectral signatures are given. The 6 first algorithm is based on the ordered topology of spectral signatures. The second 7 algorithm is based on a linear decomposition in each pixel’s neighborhood. The sought 8 after target can occupy subor above pixel. These algorithms...
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We present two new linear algorithms that perform unmixing in hyper-spectral images and then recognize their targets whose spectral signatures are given. The first algorithm is based on the ordered topology of spectral signatures. The second algorithm is based on a linear decomposition of each pixel's neighborhood. The sought after target can occupy subor above pixel. These algorithms combine i...
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A recent short communication [1] showed that an orthogonal subspace projection (OSP) classifier developed for hyperspectral image classification in [2] was equivalent to a maximum likelihood estimator (MLE) resulting from a standard method of linear unmixing. It further concluded that the MLE subsumed the OSP classifier in spite of a constant difference in their magnitudes. Coincidentally, the ...
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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 Variatio...
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ژورنال
عنوان ژورنال: Journal of the Japan society of photogrammetry and remote sensing
سال: 1996
ISSN: 0285-5844,1883-9061
DOI: 10.4287/jsprs.35.3_34