Journal ArticleDOI
Linear mixing model with scaled bundle dictionary for hyperspectral unmixing with spectral variability
TLDR
In this article, the authors proposed a linear mixing model with scaled bundle dictionary (LMM-SBD) method which combines both the scaling factors and bundle dictionary to benefit from the advantages of both approaches, and used different spatial neighbors to account for the spatial coherence of the neighboring pixels and force their corresponding abundances to have a similar sparsity pattern by adding two mixed norms to the optimization problem.About:
This article is published in Signal Processing.The article was published on 2021-11-01. It has received 4 citations till now. The article focuses on the topics: Hyperspectral imaging & Spectral signature.read more
Citations
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Journal ArticleDOI
Combining low-rank constraint for similar superpixels and total variation sparse unmixing for hyperspectral image
TL;DR: Wang et al. as mentioned in this paper proposed a novel sparse unmixing model named combining low-rank constrain for similar superpixels and total variation sparse un-mixing (CLRSS-TV) to solve the problems of low precision.
Journal ArticleDOI
Spatial Validation of Spectral Unmixing Results: A Systematic Review
TL;DR: In this paper , the authors provide an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing some papers published not only in 2011 and 2010, but also in 1996 and 1995.
Journal ArticleDOI
Global centralised and structured discriminative non‐negative matrix factorisation for hyperspectral unmixing
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Augmented GBM Nonlinear Model to Address Spectral Variability for Hyperspectral Unmixing
TL;DR: In this article , an augmented generalized bilinear model (abbreviated AGBM-SV) is proposed to address spectral variability (SV), which can effectively solve the problem of spectral variability in nonlinear mixing scenes and to improve unmixing accuracy.
References
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Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Book
Proximal Algorithms
Neal Parikh,Stephen Boyd +1 more
TL;DR: The many different interpretations of proximal operators and algorithms are discussed, their connections to many other topics in optimization and applied mathematics are described, some popular algorithms are surveyed, and a large number of examples of proxiesimal operators that commonly arise in practice are provided.
Journal ArticleDOI
Vertex component analysis: a fast algorithm to unmix hyperspectral data
TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.