Vertex component analysis: a fast algorithm to unmix hyperspectral data

By Jose M. P. Nascimento, José M. Bioucas-Dias. IEEE Transactions on Geoscience and Remote Sensing, 2005

2,650 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/26eaa00bc27aeb0a5bac4b1233697257a1ef9167

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Citations in the corpus

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “Vertex component analysis (VCA) [142] iteratively projects data on a direction orthogonal to the subspace spanned by the endmembers, which have already been determined.”
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, background): “A first step in analysing this data is to consider this multi-material problem, whereby pixels must be considered to contain mixed materials (called ‘mixed pixels’) [16, 17], and a spectral unmixing process must be applied.”
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing (2018, methodology): “2) Experimental Setup: For a fair comparison, we adopt VCA to construct the endmember dictionary for all algorithms (including the proposed ALMM) under comparison, while Hysime [43] is used to estimate the number of endmembers.”
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations (2018, methodology): “In our experiments, spectral unmixing in UBD method is implemented as follows: The number of endmembers, their spectral signatures, and their abundances at each pixel are estimated by HySime [16], vertex component analysis [45] and nonnegative constrained least squares, respectively.”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, background): “Another popular alternative, called vertex component analysis (VCA) has been proposed in [10].”
Hyperspectral Pansharpening: A Review (2015, methodology): “The matrix H can be initialized using, for example, the vertex component analysis (VCA) [59], and H and UH are then alternately optimized by minimizing ‖YH − HUH‖(2)F using Lee and Seung’s multiplicative update rules [76].”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, methodology): “A representative algorithm in this family is vertex component analysis (VCA), proposed in 2003 [25], [26].”
Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017, methodology): “We also investigated its performance with the use of VCA and adopted the one that showed better results for each data set.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, positive): “ The vertex component analysis (VCA) algorithm [123] iteratively projects data onto a direction orthogonal to the subspace spanned by the endmembers already determined.”
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data (2008, methodology): “, mp] generated by the VCA [2] algorithm.”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, methodology): “The algorithm iteratively projects data onto a direction orthogonal to a subspace spanned by the endmembers already determined.”
Nonnegative Least-Correlated Component Analysis for Separation of Dependent Sources by Volume Maximization (2010, background): “In remote sensing, many spectral unmixing algorithms [24], [25], [26], also based on the pure-source sample assumption, try to search for the purest observed pixels from the spectral data set and then identify the mixing matrix.”
Nonlinear unmixing of hyperspectral images using a generalized bilinear model (2011, methodology): “Automatic EEA include the pixel purity index [14], the N-FINDR [15], and the vertex component analysis (VCA) [16].”
Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion (2012, methodology): “First NMF of X 1a) Initialize W by VCA, and update H h by (9), with W fixed.”
Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization (2007, background): “First, this site has been extensively used for remote-sensing experiments since the 1980s, and many research works have been published with high-accuracy ground truth available [11], [35], [36]; second, the Cuprite area is a relatively undisturbed hydrothermal system with many well-exposed minerals.”