Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing
نویسندگان
چکیده
منابع مشابه
Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing
Abstract: Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may posses...
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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 material end-members. As an important constraint for NMF, sparsity has been modeled making use of the L1 regularizer. Unfortunately, the L1 re...
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Unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing. Correlation between bands of Hyperspectral images is the problem that is paid less attention to it in the unmixing algorithms. I...
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ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2019
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2019/9609302