Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

Rajabi, Roozbeh; Ghassemian, Hassan

doi:10.1007/s12524-014-0408-2

Cited by 23 publications

(9 citation statements)

References 19 publications

(16 reference statements)

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“…Nevertheless, there are mixed pixels present in a hyperspectral image due to the low spectral and spatial resolution. The mixed pixels contain more than one element, and so the spectral signature is not relevant; therefore, the accuracy of the classification will decrease [ 32 ]. The mixture models

for a number

of pixels is defined as

where

contains all the abundances of all pixels on all endmembers,

is a spectrum matrix where each column corresponds to the spectrum of an endmember,

is the number of observed bands,

is the number of endmembers and corresponding abundances and

is the additive noise [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Bilius

Pentiuc

2020

Sensors

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Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

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for a number

of pixels is defined as

where

contains all the abundances of all pixels on all endmembers,

is a spectrum matrix where each column corresponds to the spectrum of an endmember,

is the number of observed bands,

is the number of endmembers and corresponding abundances and

is the additive noise [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Bilius

Pentiuc

2020

Sensors

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“…LMM is a popular model used in hyperspectral image unmixing, which assumes that the spectral response of a pixel is a linear combination of spectral signatures (called endmembers) [19,20,[30][31][32]. Given an observed spectrum vector of a mixed pixel y ∈ R l×1 , it can be approximated by a nonnegative linear combination of m endmembers, i.e.,…”

Section: Linear Mixture Model (Lmm)mentioning

confidence: 99%

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.

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“…Nonnegative matrix factorization (NMF) [21], [22] is another practical method of unmixing, which decomposes the data into two nonnegative matrices. Recently, this basic method was developed by adding some constraints, such as the minimum volume constrained NMF (MVC-NMF) method [2], graph regularized NMF (GNMF) [23] and manifold regularized sparse NMF (GLNMF) [24]. GLNMF is a two steps approach including sparse constraint and graph regularization.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Constrained Distributed Unmixing of Hyperspectral Data

Khoshsokhan

Rajabi

Zayyani

2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing (SU) is a technique to characterize mixed pixels in hyperspectral images measured by remote sensors. Most of the spectral unmixing algorithms are developed using the linear mixing models. To estimate endmembers and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are widely used in the SU problem. One of the constraints which was added to NMF is sparsity, that was regularized by Lq norm. In this paper, a new algorithm based on distributed optimization is suggested for spectral unmixing. In the proposed algorithm, a network including single-node clusters is employed. Each pixel in the hyperspectral images is considered as a node in this network. The sparsity constrained distributed unmixing is optimized with diffusion least mean p-power (LMP) strategy, and then the update equations for fractional abundance and signature matrices are obtained. Afterwards the proposed algorithm is analyzed for different values of LMP power and Lq norms. Simulation results based on defined performance metrics illustrate the advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.

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Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

Cited by 23 publications

References 19 publications

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

Sparsity-Constrained Distributed Unmixing of Hyperspectral Data

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