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Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF

Rong Liu, Bo Du, Liangpei Zhang
2016 Remote Sensing  
) to alleviate the non-convex problem of NMF and enhance the hyperspectral unmixing accuracy.  ...  Hyperspectral unmixing aims to obtain the hidden constituent materials and the corresponding fractional abundances from mixed pixels, and is an important technique for hyperspectral image (HSI) analysis  ...  Discussion In this study, we investigated the validity of the abundance smoothness and dispersed characteristics for the NMF-based hyperspectral unmixing method.  ... 
doi:10.3390/rs8060464 fatcat:l7hvjdiforhaba4pgic55hncnq

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Similarly, an adaptive local neighborhood weight constraint was designed to propose a double abundance characteristics constrained NMF (DAC 2 NMF) [101] along with a separation constraint to prevent  ...  In this article, we aim to provide a survey on NMF-based hyperspectral unmixing.  ...  Her research focuses on hyperspectral image analysis and processing.  ... 
doi:10.1109/jstars.2022.3175257 fatcat:yqs6eizxbfghhpm4yulzczaxam

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review [article]

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza
2022 arXiv   pre-print
In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing.  ...  Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).  ...  Similarly, an adaptive local neighborhood weight constraint was designed to propose a double abundance characteristics constrained NMF (DAC 2 NMF) [101] along with a separation constraint to prevent  ... 
arXiv:2205.09933v1 fatcat:77udhvg55fdftidm554qwarqzy

Hyperspectral images unmixing based on abundance constrained multi-layer KNMF

Jing Liu, You Zhang, Yi Liu, Caihong Mu
2021 IEEE Access  
In recent years, linear hyperspectral unmixing based on non-negative matrix factorization (NMF) is attractive since NMF can simultaneously extract endmembers and abundance.  ...  L 1/2 -NMF [4] added the L 1/2 sparseness constraint to the abundance based on NMF to ensure the sparsity of abundance; multilayer nonnegative matrix factorization (MLNMF) [5] , [6] added L 1/2 constraint  ... 
doi:10.1109/access.2021.3091602 fatcat:nmdyqkncczd2nau24f4ygdpgcu

Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-based Particle Swarm Optimization

Yapeng Miao, Bin Yang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
His main research interests include hyperspectral remote sensing image analysis, machine learning, pattern recognition, and computational intelligence.  ...  Under the assumption of the LMM, each Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-based Particle Swarm Optimization Yapeng Miao and Bin Yang, Member, IEEE H pixel is approximately  ...  sparse unmixing via comprehensive learning-based particle swarm optimization (SUCPSO) can be briefly summarized in Algorithm 1.  ... 
doi:10.1109/jstars.2021.3115177 fatcat:kvpbzduq2reofpablsalvkguqm

Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

Xiang Xu, Jun Li, Shutao Li, Antonio J. Plaza
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Our experiments, carried out on both synthetic and real hyperspectral datasets, reveal that our newly proposed transform domain-based sparse NMF unmixing method (hereinafter named as 'TS-NMF') obtains  ...  Nonnegative matrix factorization (NMF)-based unmixing methods have been widely used due to their ability to extract endmembers (pure spectral signatures) and their corresponding fractional abundances in  ...  Proposed TS-NMF Unmixing Method As stated previously, imposing only nonnegative constraints on the NMF model cannot guarantee convergence.  ... 
doi:10.1109/jstars.2020.3017023 fatcat:heehd6v4lbb3xlat7via5csczy

FPGA Implementation of L1/2 Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis

Mostafa Guda, Safa Gasser, Mohamed S. El-Mahallawy, Khaled Shehata
2020 IEEE Access  
In the last decade, L 1/2 sparsity constrained Nonnegative Matrix Factorization (NMF), a linear spectral unmixing algorithm, and its extensions have been heavily studied to unmix the hyperspectral images  ...  INDEX TERMS Field-programmable gate arrays (FPGAs), hyperspectral unmixing, L 1/2 nonnegative matrix factorization algorithm (NMF). 12070 VOLUME 8, 2020  ...  In [27] , the authors introduce double abundance characteristics constrained NMF (DAC 2 NMF).  ... 
doi:10.1109/access.2020.2966044 fatcat:rsvuk6tizndtdch7aasvp2lwpq

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence [article]

E.M.M.B. Ekanayake, H.M.H.K. Weerasooriya, D.Y.L. Ranasinghe, S. Herath, B. Rathnayake, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, H.M.V.R. Herath
2021 arXiv   pre-print
Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances.  ...  Imposing this constraint on the conventional NMF framework promotes the extraction of independent endmembers while further enhancing the parts-based representation of data.  ...  Abstract-Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances  ... 
arXiv:2003.01041v5 fatcat:jys4xvzs4vepddyflgttqbcbby

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Genping Zhao, Fei Li, Xiuwei Zhang, Kati Laakso, Jonathan Cheung-Wai Chan
2021 Remote Sensing  
Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal.  ...  Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures.  ...  Since the abundance matrix represents the proportion of the endmembers in mixed pixels, its column vector satisfies the Abundance Nonnegative Constraint (ANC) and Abundance Sum-to-one Constraint (ASC).  ... 
doi:10.3390/rs13204102 fatcat:3wri2fguxbhvbanhjmu2j7zdeu

Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing Incorporating Endmember Independence

Mevan Ekanayake, Hashan Kavinga Weerasooriya, Yasiru Ranasinghe, Sanjaya Herath, Bhathiya Rathnayake, Roshan Godaliyadda, Vijitha Herath, Mervyn Ekanayake
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Abstract-Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances  ...  sparse NMF [7] , Double Constrained NMF [44] , total variation regularized reweighted sparse NMF (TV-RSNMF) [45] , subspace clustering constrained sparse NMF (SC-NMF) [46] , nonsmooth NMF (nsNMF)  ... 
doi:10.1109/jstars.2021.3126664 fatcat:pwdnsv23tfchnbym6wjrgkv2qm

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [article]

José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot
2012 arXiv   pre-print
Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel.  ...  Algorithm characteristics are illustrated experimentally.  ...  Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures  ... 
arXiv:1202.6294v2 fatcat:4vxq62jxvzfynpb75wvvhw4phq

A Sturdy Nonlinear Hyperspectral Unmixing Algorithm Using Iterative Block- Coordinate Descent Algorithm

Majeti Sireesha, Panchala Naganjaneyulu, Kaparapu Babulu
2019 International Journal of Intelligent Engineering and Systems  
Accompanying the traditional nonnegativity and sum-to-one restraints underlying to the spectral mixing, this proposed model heads to a novel pattern of sturdy nonnegative matrix factorization (S-NMF) with  ...  Moreover, distinctive hyperspectral mixture models also presented by adopting the considerations like NEs, mismodelling effects (MEs) and endmember variability (EV).  ...  Note finally that other articles have addressed hyperspectral unmixing with regular NMF (i.e., in the standard linear model).  ... 
doi:10.22266/ijies2019.0630.18 fatcat:rd3pxxiq75htnnadxlo3ksiw4q

Hyperspectral Sparse Unmixing With Spectral-Spatial Low-Rank Constraint

Fan Li, Shaoquan Zhang, Bingkun Liang, Chengzhi Deng, Chenguang Xu, Shengqian Wang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this work, the double weighting factors under the ℓ1 framework aim to improve the row sparsity of the abundance matrix and the sparsity of each abundance map.  ...  Spectral unmixing is a consequential preprocessing task in hyperspectral image interpretation.  ...  (NMF)-based algorithms [9] - [11] achieve remarkable performance, among which sparse unmixing [12] relying on spectral libraries becomes one of the most active research topics.  ... 
doi:10.1109/jstars.2021.3086631 fatcat:cg7ggc4lqbg2jnglgyfvvyocia

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection  ...  These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters.  ...  The types of structured sparsity exploited in HU are directly linked with two characteristics of hyperspectral data: i) the fractional abundance maps are piecewise smooth; and ii) the fractional abundance  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

Self-Dictionary Regression for Hyperspectral Image Super-Resolution

Dongsheng Gao, Zhentao Hu, Renzhen Ye
2018 Remote Sensing  
Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI.  ...  Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution.  ...  Computational Complexity Based on the multiplicative updates, the overall cost for NMF is O (tNLp).  ... 
doi:10.3390/rs10101574 fatcat:ml2tudq55zbibln72ocdfwoud4
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