Does independent component analysis play a role in unmixing hyperspectral data?

By Jose M. P. Nascimento, José M. Bioucas-Dias. IEEE Transactions on Geoscience and Remote Sensing, 2003

413 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Media Technology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1d10936a91dd0d570c764b5dffc67b6421b9b5fe

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In Signal Processing

Citations in the corpus

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, result): “While this observation is true, and in fact has been noticed for a while [16], classical BSS methods established in the SP field usually do not fall in any of the mainstream blind HU approaches.”
Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, background): “However, this assumption of independence cannot be fulfilled, since it violates the truth that the sum of proportion fractions within each pixel is constant [12].”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, neutral): “dependence between the different abundances [24].”
Sparse Unmixing of Hyperspectral Data (2011, background): “Thus, the sources are statistically dependent, which compromises the performance of independent component analysis algorithms in hyperspectral unmixing [8].”
Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing (2010, neutral): “, the fractional abundances of the spectra/materials present in the data) exhibit statistical dependency [15].”
Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization (2007, methodology): “ponent analysis (PCA) [3], independent component analysis (ICA) [4], [5], and singular value decomposition (SVD) [6].”
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data (2008, methodology): “Some popular algorithms taking this assumption are the the N-FINDR [10], the the pixel purity index (PPI) [11], the Automated Morphological Endmember Extraction (AMEE) [12], the vertex component analysis (VCA) [4], and the simplex growing algorithm (SGA) [13].”
Hyperspectral Image Classification With Independent Component Discriminant Analysis (2011, background): “Due to its attractive properties, ICA is receiving a growing interest among the remote sensing community for feature reduction and spectral unmixing [52]–[54].”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “This dependence compromises ICA applicability to hyperspectral data as shown in [39], [158].”
A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, methodology): “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].”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “This dependence compromises ICA applicability to hyperspectral data as shown in [42].”
Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009, methodology): “, the sum of abundance fractions associated with each pixel is constant [15].”