Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery
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...
متن کاملNonnegative Matrix and Tensor Factorization
T here has been a recent surge of interest in matrix and tensor factorization (decomposition), which provides meaningful latent (hidden) components or features with physical or physiological meaning and interpretation. Nonnegative matrix factorization (NMF) and its extension to three-dimensional (3-D) nonnegative tensor factorization (NTF) attempt to recover hidden nonnegative common structures...
متن کاملHyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization
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...
متن کاملAdvances in Nonnegative Matrix and Tensor Factorization
1 Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Saitama 351-0198, Japan 2Department of Informatics and Mathematical Modeling, Technical University of Denmark, Richard Petersens Plads, Building 321, 2800 Lyngby, Denmark 3Advanced Technology Labs, Adobe Systems Inc., 275 Grove Street, Newton, MA 02466, USA 4Centre for Vision, Speech, and Signal Processing, Univer...
متن کاملSpectral Signal Unmixing with Interior-Point Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) is an unsupervised learning method that has been already applied to many applications of spectral signal unmixing. However, its efficiency in some applications strongly depends on optimization algorithms used for estimating the underlying nonnegatively constrained subproblems. In this paper, we attempt to tackle the optimization tasks with the inexact Inte...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2017
ISSN: 0196-2892,1558-0644
DOI: 10.1109/tgrs.2016.2633279