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Random Hadamard Projections for Hyperspectral Unmixing
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DOI：10.1109/LGRS.2016.2647001.png)
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Abstract
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Dimensionality reduction based on random projections is investigated in the context of spectral unmixing of hyperspectral imagery with aims toward unmixing accuracy and computational efficiency. To this end, both Hadamard-based random projections-which significantly reduce computational costs with respect to more traditional Gaussian-driven projections-as well as a fast singular value decomposition deployed within a random-projection space are considered. Experimental results reveal that the methods based on Hadamard random projections offer abundance-estimation performance superior to other methods in conjunction with significantly reduced computational complexity.
Keywords:
Dimensionality reduction
Hadamard matrix (HM)
hyperspectral ummixing
random projection
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