A two-stage method for spectral-spatial classification of hyperspectral images

DOI10.1007/s10851-019-00925-9zbMath1483.68282arXiv1806.00836OpenAlexW3010011727MaRDI QIDQ2203358

Mila Nikolova, Kelvin K. Kan, Robert J. Plemmons, Raymond Honfu Chan

Publication date: 6 October 2020

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.00836

zbMATH Keywords

image segmentationimage denoisingalternating direction method of multipliersMumford-Shah modelsupport vector machinehyperspectral image classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

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Uses Software

• LIBSVM

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nonlinear total variation based noise removal algorithms
• A variational approach to remove outliers and impulse noise
• Support-vector networks
• Automated regularization parameter selection in multi-scale total variation models for image restoration
• A note on Platt's probabilistic outputs for support vector machines
• A three-stage approach for segmenting degraded color images: smoothing, lifting and thresholding (SLaT)
• Spatially dependent regularization parameter selection in total generalized variation models for image restoration
• A Two-Stage Image Segmentation Method Using a Convex Variant of the Mumford--Shah Model and Thresholding
• A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise
• Optimal approximations by piecewise smooth functions and associated variational problems
• From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
• Active contours without edges
• High-Order Total Variation-Based Image Restoration
• Euler's Elastica and Curvature-Based Inpainting
• Parameter selection for total-variation-based image restoration using discrepancy principle
• An Introduction to Iterative Toeplitz Solvers
• An Infeasible Primal-Dual Algorithm for Total Bounded Variation--Based Inf-Convolution-Type Image Restoration
• Signal Recovery by Proximal Forward-Backward Splitting
• Total Generalized Variation