Hyperspectral Subspace Identification

By José M. Bioucas-Dias, Jose M. P. Nascimento. IEEE Transactions on Geoscience and Remote Sensing, 2008

1,139 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/45f2bd821d663e07d11bf8d4371a7fb4f0fa5e2c

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Citations in the corpus

Sparse Unmixing of Hyperspectral Data (2011, methodology): “Once the simulated data set was generated, we used the HySime algorithm [46] to find the signal subspace and projected the data on this subspace.”
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016, methodology): “In this case, we resort to the HSI subspace identification method called HySime, as proposed in [47], to estimate the number of endmembers.”
SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral Image Super-Resolution (2023, methodology): “After projected into the lowdimensional subspace detected by HiSime [48], although this”
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery (2009, methodology): “Here, the PCA-based dimension reduction step introduced in paragraph III-B-2 was replaced by two techniques: the well-known MNF transform [44] approach and the more recent HySime algorithm [55].”
Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, methodology): “One can conduct it by using virtual dimensionality [32] or HySime [33].”
Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011, methodology): “Since the estimation of the number of end-members in the scene is crucial in the unmixing process, here, we resort to the HySime algorithm [44].”
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data (2008, methodology): “, [17]) has been applied to the data set and the vectors yi ∈ R are represented in the signal subspace spanned by the endmember spectral signatures.”
Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): “, HySime [50]) to estimate the range of values for the rank, and then choose the best desired rank value among the candidates for the spectral mode.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, neutral): “The signal and noise correlation matrices were obtained with the algorithms and code distributed with HySime [83].”
Endmember variability in Spectral Mixture Analysis: A review (2011, methodology): “Based on the radiosity theory described by Borel and Gerstl (1994), nonlinear mixing effects can be modelled explicitly by including additional endmembers to the LMM, each accounting for a characteristic interaction among ground objects.”
Hyperspectral Remote Sensing Data Analysis and Future Challenges (2013, methodology): “The signal subspace was identified with the HySime [58] algorithm and the original data projected onto this subspace.”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “Low-rank based techniques have also been utilized for mixed noise removal in HSIs [59–62].”
A variable splitting augmented Lagrangian approach to linear spectral unmixing (2009, background): “, [19]) and that the observed vectors Y «, i = 1, .”
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing (2018, methodology): “(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): “The subspace dimension input to “PCA+BM4D” was 6, which is estimated by HySime [16].”