Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization

By Lidan Miao, Hairong Qi. IEEE Transactions on Geoscience and Remote Sensing, 2007

933 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6ad6afe56f793a3a4facaa9e2dd2ec3622f1940b

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Citations in the corpus

Sparse Unmixing of Hyperspectral Data (2011, neutral): “…morphological endmember extraction [18], iterated constrained endmembers (ICE) [19], minimum volume constrained non-negative matrix factorization [20], spatial–spectral endmember extraction [21], sparsity-promoting ICE [22], minimum volume simplex analysis [23], and simplex identiﬁcation via…”
Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, methodology): “For the real hyperspectral data, the four counterparts are L1/2-NMF, VCA, MVCNMF, and simplex identification via split augmented Lagrangian (SISAL) [35].”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, background): “Specifically, we carry out the following heuristic: for , , ,k N1 f= determine an estimate IEEE SIGNAL PROCESSING MAGAZINE [74] JANUARY 2014 { [ ], , [ ]},arg maxb P f b x x L1s.t. conv b Fk k k2 2”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “This is the case for the iterative constrained endmember (ICE) [144] and minimum volume constrained nonnegative matrix factorization approaches [145], the main differences of which are related to the way they define the data volume term.”
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “We have illustrated the advantages of our unmixing method on synthetic and real-world data and compared our method against a number of alternatives, i.e., NMF, L 1 -NMF and L 2 -NMF, VCA, MVC-NMF, PSNMFSC and SISAL.”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, background): “(45) March 17, 2009 DRAFT 22 The parameter σ2 can be tuned to choose the SNR whereas the parameter η adjusts the shape width (η → ∞ corresponds to i.i.d. noise).”
The Why and How of Nonnegative Matrix Factorization (2014, methodology): “…implementation of reﬁned NMF models for various applications is a very active area of research, e.g., graph regularized NMF [16], orthogonal NMF [24], tri-NMF [38, 85], semi and convex NMF [36], projective NMF [110], minimum volume NMF [93], and hierarchical NMF [86], to cite only a few.”
Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing (2010, positive): “Therefore, finding the endmembers amounts to identifying the vertices of that simplex [18, 16, 3, 13 , 20, 1]. Sparse regression is another direction recently explored for HU [9], which has connections with both the statistical and the geometrical frameworks.”
A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, background): “Given a set of mixed hyperspectral vectors, linear mixture analysis, or linear unmixing, aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their abundance fractions [1,2,3,4,5,6].”
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data (2008, methodology): “The effectiveness of the new method was illustrated in a limited comparison with the state-of-the-art non-negative matrix factorization method [5], where the MVSA method yielded very competitive results.”
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, methodology): “The creation of abundances is similar to that in [43], which takes three steps: First, the scene, with a size of z(2) × z(2) (z ∈ Z) pixels, is divided into z × z regions.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, positive): “The iterative constrained endmembers (ICE) algorithm [137] aims at solving an optimization problem similar to that of MVC-NMF, where the volume of the simplex is replaced by a much more manageable approximation: the sum of squared distances between all the simplex vertices.”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “This is the case of iterative constrained endmembers (ICE) algorithm [50] and of the minimum volume transform-nonnegative matrix factorization (MVC-NMF) [51], whose main differences are related with the way they define the regularizer M ( ) V .”