Sparse Unmixing of Hyperspectral Data

By Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio J. Plaza. IEEE Transactions on Geoscience and Remote Sensing, 2011

1,059 citations recorded by the source corpus.

Subfields: Computational Mechanics, Media Technology

View Paper: https://www.semanticscholar.org/paper/41f426e2dc5b4944148014d9c86d20fdd21dc968

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Media Technology

Citations in the corpus

Hyperspectral Image Super-Resolution via Non-local Sparse Tensor Factorization (2017, methodology): “The matrix factorization based super-resolution methods assume each pixel of the target HR-HSI can be written as the linear combination of a small number of distinct spectral signatures [12].”
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation (2016, background): “…that each pixel z i ∈ R L of the target image Z can be written as the linear combination of a small number of distinct spectral signatures [29], [30] -i.e., z i = Dα i + e i , (2) where D ∈ R L×K + (K ≥ L) is the spectral dictionary, α i ∈ R K + is the fractional abundance vector assumed to be…”
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014, background): “The low-rank property of an HSI can be exploited from the perspective of a linear spectral mixing model [27], [28].”
Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, methodology): “Assuming that the HR-HSI only contains a small number of pure spectral signatures [29], these approaches ﬁrst unfold the HR-HSI as a matrix and then factor it into a basis matrix and a coefﬁcient matrix.”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, background): “In the SR formulation, it is assumed that the measured spectral vectors can be expressed as linear combinations of a small number of pure spectral signatures known in advance [6] (e.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “The applicability of sparse regression to HU was studied in detail in [173].”
Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features (2013, methodology): “In [33] and [34], the linear sparse regression is used to find an optimal subset of signatures from a very large spectral library, which can best estimate the endmembers and the corresponding”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, background): “To address these issues, sparse unmixing [161] expresses pixel vectors as linear combinations of a few pure spectral signatures obtained from a potentially very large spectral library of ground materials.”
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016, methodology): “The TV-based algorithm is a very popular denoising approach because of its effectiveness in preserving edge information and the spatial piecewise smoothness [13], [14].”
Deep Hyperspectral Image Sharpening (2018, methodology): “Matrix factorization-based HSI sharpening methods assume that the HR-HSI contains a small number of pure spectral signatures [43], and thus, the HR-HSI can be approxi-”
Collaborative Sparse Regression for Hyperspectral Unmixing (2014, methodology): “The unmixing problem is solved by NCLS, SUn- SAL (which proved to outperform other methods which do not impose sparsity explicitly, see [16]) and CLSUnSAL.”
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing (2012, methodology): “Plot of diagonal values of the correction matrix C for the following spectral libraries: (a) A 1 , and (b) A. problem by alternating the minimization with respect to C and to X [44].”
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, background): “More recent works on this topic include the semisupervised algorithms based on sparse regression in [24] and [29].”
A Review of Nonlinear Hyperspectral Unmixing Methods (2014, positive): “It must be noted that sparse linear unmixing techniques [189]–[192] can be considered as piece-wise linear methods as well.”
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing (2014, background): “There is a large amount of experimental evidence that indicates that nonnegative 1 , relaxation can yield useful unmixing results [2], [50], [51].”