L1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing
Hyperspectral unmixing is a crucial preprocessing step for material classification and recognition. In the last decade, nonnegative matrix factorization (NMF) and its extensions have been intensively studied to unmix hyperspectral imagery and recover the
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|Source:||Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2010)|
|01_Qian_L1/2_Sparsity_Constrained_2010.pdf||168.83 kB||Adobe PDF||Request a copy|
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