Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data

Khoshsokhan, Sara; Rajabi, Roozbeh; Zayyani, Hadi

doi:10.1117/1.jrs.13.026509

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…Local neighborhood weights have been introduced into the NMF problem in [11]. Some other structured NMF methods were also proposed to incorporate spatial information into the problem [12], [13], [14]. In addition to these methods, a large number of spatially constrained NMF methods belong to the total variation (TV) based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing Using a General $\ell _{q}$ Norm Regularization

Gholinejad,

Amiri-Simkooei

2024

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

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Hyperspectral unmixing (HU), an essential procedure for various environmental applications, has garnered significant attention within remote sensing communities. Among different groups of HU methods, non-negative matrix factorization (NMF)-based ones have gained widespread popularity due to their high capability in simultaneously extracting endmembers and their corresponding abundances. However, converting 3D hyperspectral data cube into 2D matrix format leads to the loss of spatial and potential correlation information. Consequently, in recent years, non-negative tensor factorization (NTF) methods, which preserve the 3D nature of hyperspectral data cube, have been extensively embraced by numerous researchers. Nevertheless, incorporating prior information into NTF-based problems faces limitations owing to the inconsistency of such information, particularly concerning ℓ1 norm sparsity and the abundance sum-to-one constraint (ASC). To address this limitation, our study introduces a novel general regularization term. This term leverages sparsity and ASC simultaneously, integrating it into a matrix-tensor factorization framework. Our proposed method, named matrix-tensor based hyperspectral unmixing method with general ℓq norm regularization (MTUHLq), is established on the block term decomposition (BTD) paradigm, which ensures physical interpretability and simple implementation. To investigate the performance of the proposed MTUHLq, a series of experiments on both synthetic and real hyperspectral data sets were conducted. The results of the implemented experiments indicated that the proposed method outperformed other stateof-the-art hyperspectral unmixing methods.

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Section: Introductionmentioning

confidence: 99%

Combinatorial Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing Using a General $\ell _{q}$ Norm Regularization

Gholinejad,

Amiri-Simkooei

2024

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

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“…12,13 In the clustered multitask NMF algorithm the nodes in the network are clustered by fuzzy c-means method and diffusion LMP strategy has been used to define the cost function. 14 Spectral-spatial constrained sparse unmixing (SSCSUn) describes a method in which a total variation (TV) regulator is used as a spatial weight factor to more effectively utilize spatial information. 15 An innovative linear method that is based on l 1 − l 2 sparsity and TV regularization has been developed to ameliorate the accuracy of hyperspectral unmixing.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, a network consisting of single node clusters is used, so that each hyperspectral pixel is considered as a node where the sparsity constraint expressed with diffusion least mean p-power (LMP) strategy is optimized 12 , 13 . In the clustered multitask NMF algorithm the nodes in the network are clustered by fuzzy c-means method and diffusion LMP strategy has been used to define the cost function 14 …”

Section: Introductionmentioning

confidence: 99%

Spectral unmixing of hyperspectral images based on block sparse structure

Azarang

Rajabi

Zayyani

et al. 2023

J. Appl. Rem. Sens.

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Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. SU is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember. Block-sparsity exists in hyperspectral images as a result of spectral similarity between neighboring pixels. In block-sparse signals, the nonzero samples occur in clusters and the pattern of the clusters is often supposed to be unavailable as prior information. This paper presents an innovative SU approach for HSIs based on block-sparse structure. Hyperspectral unmixing problem is solved using pattern coupled sparse Bayesian learning strategy. To evaluate the performance of the proposed SU algorithm, it is tested on both synthetic and real hyperspectral data and the quantitative results are compared to those of other state-of-the-art methods in terms of abundance angle distance and mean squared error. The achieved results show the superiority of the proposed algorithm over the other competing methods by a significant margin.

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“…Inspired by the denoising method [38], Lu et al proposed a structure constrained sparse NMF (CSNMF) method [39] which exploited clustering based approach to find the potential structure information. In [40], a clustered multitask network was proposed to solve the unmixing problem, which also used the clustering method to explore the distribution. Recently, spatial group sparsity regularized NMF (SGSNMF) [41] utilized superpixels that are obtained from image segmentation as a spatial prior to promote hyperspectral unmixing.…”

Section: Introductionmentioning

confidence: 99%

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

Zhou

Zhang

Wang

et al. 2020

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

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Hyperspectral unmixing is a crucial task for hyperspectral images (HSI) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF) and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this paper, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.

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Clustered multitask non-negative matrix factorization for spectral unmixing of hyperspectral data

Cited by 12 publications

References 33 publications

Combinatorial Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing Using a General $\ell _{q}$ Norm Regularization

Combinatorial Nonnegative Matrix-Tensor Factorization for Hyperspectral Unmixing Using a General $\ell _{q}$ Norm Regularization

Spectral unmixing of hyperspectral images based on block sparse structure

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

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