Corpus ID: 235694247

SISAL Revisited

@inproceedings{Huang2021SISALR,
  title={SISAL Revisited},
  author={Chu-Hsiang Huang and Mingjie Shao and Wing-Kin Ma and Anthony Man-Cho So},
  year={2021}
}
Simplex identification via split augmented Lagrangian (SISAL) is a popularly-used algorithm in blind unmixing of hyperspectral images. Developed by José M. Bioucas-Dias in 2009, the algorithm is fundamentally relevant to tackling simplex-structured matrix factorization, and by extension, non-negative matrix factorization, which have many applications under their umbrellas. In this article, we revisit SISAL and provide new meanings to this quintessential algorithm. The formulation of SISAL was… Expand

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References

SHOWING 1-10 OF 36 REFERENCES
Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution
TLDR
A hybrid inexact block coordinate descent (HiBCD) scheme wherein one coordinate is updated via the fast proximal gradient method, while another via the Frank-Wolfe (FW) method is introduced, which is computationally much more efficient than the state-of-the-art CoSMF schemes in HSR. Expand
Hyperspectral Unmixing Based on Mixtures of Dirichlet Components
TLDR
A cyclic minimization algorithm is developed where the number of Dirichlet modes is inferred based on the minimum description length principle, thus automatically enforcing the constraints on the abundance fractions imposed by the acquisition process. Expand
A variable splitting augmented Lagrangian approach to linear spectral unmixing
  • J. Bioucas-Dias
  • Mathematics, Computer Science
  • 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
  • 2009
TLDR
This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL), which is very fast and able to solve problems far beyond the reach of the current state-of-the art algorithms. Expand
Learning dependent sources using mixtures of Dirichlet: Applications on hyperspectral unmixing
  • J. Nascimento, J. Bioucas-Dias
  • Mathematics, Computer Science
  • 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
  • 2009
TLDR
This paper is an elaboration of the DECA algorithm to blindly unmix hyperspectral data, where the abundance fractions are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. Expand
On Hyperspectral Unmixing
  • Wing-Kin Ma
  • Computer Science, Engineering
  • 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
  • 2021
In this article the author reviews Jose Bioucas-Dias' key contributions to hyperspectral unmixing (HU), in memory of him as an influential scholar and for his many beautiful ideas introduced to theExpand
A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
TLDR
This paper incorporates convex analysis and Craig's criterion to develop a minimum-volume enclosing simplex (MVES) formulation for hyperspectral unmixing, and provides a non-heuristic guarantee of the MVES problem formulation, where the existence of pure pixels is proved to be a sufficient condition for MVES to perfectly identify the true endmembers. Expand
Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing
TLDR
This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation and observes that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Expand
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No-Pure-Pixel Case
TLDR
It is proved that MVES is indeed robust against lack of pure pixels, as long as the pixels do not get too heavily mixed and too asymmetrically spread. Expand
Blind Separation of Quasi-Stationary Sources: Exploiting Convex Geometry in Covariance Domain
TLDR
This paper revisits blind source separation of instantaneously mixed quasi-stationary sources (BSS-QSS) and shows that VolMin guarantees perfect mixing system identifiability under an assumption more relaxed than (exact) local dominance - which means wider applicability in practice. Expand
A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
TLDR
This paper considers regularized block multiconvex optimization, where the feasible set and objective function are generally nonconvex but convex in each block of variables and proposes a generalized block coordinate descent method. Expand
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