A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing

DOI10.1109/TSP.2009.2025802zbMath1392.94129OpenAlexW2050041778MaRDI QIDQ4569969

Tsung-Han Chan, Chong-Yung Chi, Wing-Kin Ma, Yu-Min Huang

Publication date: 9 July 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tsp.2009.2025802

Mathematics Subject Classification ID

Convex programming (90C25) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (14)

On statistical learning of simplices: unmixing problem revisited ⋮ Enhancing Pure-Pixel Identification Performance via Preconditioning ⋮ Backtracking-based simultaneous orthogonal matching pursuit for sparse unmixing of hyperspectral data ⋮ On endmember identification in hyperspectral images without pure pixels: a comparison of algorithms ⋮ Sets that maximize probability and a related variational problem ⋮ Geodesic simplex based multiobjective endmember extraction for nonlinear hyperspectral mixtures ⋮ Alternating direction method of multipliers for linear hyperspectral unmixing ⋮ A new convex model for linear hyperspectral unmixing ⋮ Minimal Volume Simplex (MVS) Polytopic Model Generation and Manipulation Methodology for TP Model Transformation ⋮ Uniqueness of Nonnegative Matrix Factorizations by Rigidity Theory ⋮ A lattice matrix method for hyperspectral image unmixing ⋮ A dual symmetric Gauss-Seidel alternating direction method of multipliers for hyperspectral sparse unmixing ⋮ Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization ⋮ SISAL Revisited

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