☆ 4.7 Review

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

Taner Ince

Summary: An efficient spatial regularization method using superpixel segmentation and graph Laplacian regularization is proposed for the sparse hyperspectral unmixing method, which shows superiority over well-known algorithms in literature through experimental results on simulated and real data sets.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

Blind Unmixing of Hyperspectral Images Based on L1 Norm and Tucker Tensor Decomposition

Le Sun et al.

Summary: A novel blind unmixing method for hyperspectral images based on tucker tensor decomposition and L-1 regularization term was proposed, outperforming the latest method ULTRA-V in comparative experiments on two simulation datasets.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

A General Loss-Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

Jiangtao Peng et al.

Summary: This letter proposes a general loss-based NMF (GLNMF) model for hyperspectral unmixing. By introducing a robust loss function, the model improves its robustness and achieves higher accuracy compared to existing NMF methods, as demonstrated through experimental results on simulated and real data sets.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Engineering, Electrical & Electronic

Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

Burkni Palsson et al.

Summary: This article explores the application of deep learning, specifically autoencoders, in remote sensing, focusing on blind hyperspectral unmixing. Through comparative experiments with 11 different autoencoder methods and one traditional method, as well as extensive ablation experiments with a simple spectral unmixing autoencoder, the article unravels the power of autoencoder methods and examines the impact of implementation details on the results.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

Self-Supervised Robust Deep Matrix Factorization for Hyperspectral Unmixing

Heng-Chao Li et al.

Summary: A new self-supervised robust deep matrix factorization (SSRDMF) model is proposed for hyperspectral unmixing, consisting of an encoder and a decoder to address challenges in hyperspectral image processing. By explicitly modeling sparse noise and including a self-supervised constraint for spectral information exploration, the proposed method achieves superior unmixing performance compared to other state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

SNMF-Net: Learning a Deep Alternating Neural Network for Hyperspectral Unmixing

Fengchao Xiong et al.

Summary: This article presents an end-to-end deep neural network named SNMF-Net for hyperspectral unmixing, which combines model-based and learning-based methods. SNMF-Net shows high physical interpretability and performance advantages over state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Computer Science, Artificial Intelligence

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

Danfeng Hong et al.

Summary: This paper proposes a deep learning approach called endmember-guided unmixing network (EGU-Net) to improve the efficiency and accuracy of hyperspectral unmixing. By utilizing a two-stream Siamese deep network and adding spectrally meaningful constraints, EGU-Net is able to extract endmembers better and achieve more accurate and interpretable unmixing solutions.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

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Article Geochemistry & Geophysics

ADMM-Based Hyperspectral Unmixing Networks for Abundance and Endmember Estimation

Chao Zhou et al.

Summary: This article proposes a new algorithm for hyperspectral image (HSI) unmixing that combines model- and learning-based techniques, delivering improved performance. The algorithm unrolls a solver for a sparse regression problem and utilizes neural networks for abundance estimation and blind unmixing. Experimental results show faster convergence and better performance compared to other methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Computer Science, Artificial Intelligence

Endmember independence constrained hyperspectral unmixing via nonnegative tensor factorization

Jin-Ju Wang et al.

Summary: In this paper, a new NTF-based model called EIC-NTF is proposed for hyperspectral unmixing to mitigate the impact of high correlation among endmembers and abundances. The model introduces endmember independence constraint for endmember estimation and exploits the low-rankness in abundance maps for abundance estimation. Experimental results show that the proposed algorithm is effective for hyperspectral unmixing.

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A novel hyperspectral unmixing model based on multilayer NMF with Hoyer's projection

Yuan Yuan et al.

Summary: Hyperspectral remote sensing is an important method for earth observation, but the low spatial resolution of images makes it hard to distinguish ground objects. The HP-MLNMF framework proposed in this paper shows promising results in improving unmixing speed and efficiency in spectral signature estimation.

NEUROCOMPUTING (2021)

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Article Computer Science, Artificial Intelligence

Dual-Constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior

Yan Zhang et al.

Summary: A new dual-constrained deep semi-supervised coupled factorization network is proposed in this paper for learning hierarchical representations. It integrates error correction mechanism and feature fusion strategy to improve representation ability, enriches prior knowledge through coefficients-based label prediction, and incorporates additional label and structure constraints to enhance discriminating abilities. Integration of adaptive dual-graph learning and fine-tuning process further improve the accuracy of representations.

INTERNATIONAL JOURNAL OF COMPUTER VISION (2021)

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Article Environmental Sciences

Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

Xiaochen Lv et al.

Summary: Hyperspectral unmixing is a crucial technique for analyzing remote sensing images to obtain endmembers and abundances. The proposed Cluster-wise Weighted NMF method in this paper aims to utilize the information of imbalanced samples in hyperspectral data during the unmixing procedure.

REMOTE SENSING (2021)

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LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing

Min Zhao et al.

Summary: This paper proposes a nonsymmetric autoencoder network for blind hyperspectral unmixing, which takes advantage of the universal modeling ability of deep neural networks to learn nonlinear relations from data. It includes an LSTM structure and spatial regularization to capture spectral and spatial correlation information, and an attention mechanism to enhance unmixing performance. Experiments validate the effectiveness of the proposed method using synthetic and real data.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2021)

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Article Geochemistry & Geophysics

Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing

Jiangtao Peng et al.

Summary: This article introduces three self-paced learning based NMF unmixing models that use weighted least-squares losses instead of the least-squares loss in the standard NMF model. By adopting a self-paced learning strategy to adaptively learn weights, the SpNMF models demonstrate better accuracy and robustness in handling different types of noise in hyperspectral data. Gradually enlarging the model set and selecting atoms from easy to complex allows SpNMF to exclude noisy or outlying atoms, resulting in a more robust unmixing model.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Geochemistry & Geophysics

Correntropy-Based Spatial-Spectral Robust Sparsity-Regularized Hyperspectral Unmixing

Xiaorun Li et al.

Summary: Hyperspectral unmixing is a crucial technique for exploiting spectral signatures and their abundances in remotely sensed data. Existing robust methods often assume noise only exists in one form, but in reality, HSIs are corrupted by multiple types of noise simultaneously. This article proposes a robust unmixing model with spatial-spectral constraints and adaptive weighted sparsity for abundances.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Geochemistry & Geophysics

SpectralSpatial Joint Sparse NMF for Hyperspectral Unmixing

Le Dong et al.

Summary: In this article, a novel unmixing method is proposed using two types of self-similarity to constrain sparse NMF. The method explores spatial global self-similarity groups between pixels based on the whole image and creates spectral local self-similarity groups inside superpixels. By sparsely encoding pixels within each spatial and spectral group, the method forces the abundance of pixels within each group to be similar, thereby constraining the NMF unmixing framework. Experiments demonstrate the superiority of this method over existing methods on synthetic and real data.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Double Weighted Sparse Nonnegative Tensor Factorization for Hyperspectral Unmixing

Heng-Chao Li et al.

Summary: This letter proposes a new double weighted sparse NTF (DWSNTF) unmixing method to make the most of spatial information and abundance sparsity. By utilizing a double weighted L-1 regularizer, more precise and sparse abundance maps can be characterized, while preserving more details and preventing oversmoothness. The experimental results demonstrate the validity and superiority of the proposed method against the state-of-the-art methods on both synthetic and real data.

INTERNATIONAL JOURNAL OF REMOTE SENSING (2021)

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Multiview Concept Learning Via Deep Matrix Factorization

Wei Zhao et al.

Summary: Multiview representation learning leverages information from multiple views to summarize the consistency and complementarity in multiview data, but previous methods often neglect complex hierarchical information. The deep multiview concept learning (DMCL) method hierarchically factorizes multiview data to explicitly model consistent and complementary information, capturing semantic structures at the highest abstraction level.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Spectral Variability in Hyperspectral Data Unmixing

Ricardo Borsoi et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2021)

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A survey on deep matrix factorizations

Pierre De Handschutter et al.

Summary: Constrained low-rank matrix approximations are powerful linear dimensionality reduction techniques, but unable to mine complex, interleaved features. Deep matrix factorization has been introduced to extract multiple layers of features and has shown outstanding performances in unsupervised tasks. It is likely to become an important paradigm in unsupervised learning in the next few years.

COMPUTER SCIENCE REVIEW (2021)

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Article Engineering, Electrical & Electronic

A Blind Spectral Unmixing in Wavelet Domain

S. S. Vijayashekhar et al.

Summary: A wavelet-based energy minimization framework is developed to jointly estimate endmembers and abundances without assuming pure pixels, while considering noisy scenarios. Experimental results demonstrate that the algorithm performs well on various types of hyperspectral datasets.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Article Engineering, Electrical & Electronic

Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral Images

Ge Zhang et al.

Summary: This study introduces a spectral-spatial constrained unmixing method based on nonnegative matrix factorization (NMF), which improves the performance of unmixing by imposing spatial and spectral constraints. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art NMF-based unmixing algorithms.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Hyperspectral Images Unmixing Based on Abundance Constrained Multi-Layer KNMF

Jing Liu et al.

Summary: A novel nonlinear unmixing method, AC-MLKNMF, has been proposed for hyperspectral image analysis, which outperforms other methods in terms of unmixing accuracy and provides more accurate results based on the spatial distribution characteristics of actual ground objects.

IEEE ACCESS (2021)

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Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing

Pan Zheng et al.

Summary: This article introduces a sparse and low-rank constrained tensor factorization unmixing algorithm based on MV-NTF framework, which aims to improve the accuracy and applicability of abundance maps by imposing sparse constraint and low-rank regularization.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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A manifold Hessian-regularized NMF for hyperspectral data unmixing

Tianliang Peng et al.

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Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability

Tales Imbiriba et al.

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Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing

Xiaoqiang Lu et al.

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Xiang Xu et al.

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Combined Nonlocal Spatial Information and Spatial Group Sparsity in NMF for Hyperspectral Unmixing

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Tatsumi Uezato et al.

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Lei Tong et al.

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