Enhancing Spectral Unmixing by Local Neighborhood Weights

Liu, Junmin; Zhang, Jiangshe; Gao, Yuelin; Zhang, Chunxia; Li, Zhihua

doi:10.1109/jstars.2012.2199282

Cited by 59 publications

(63 citation statements)

References 22 publications

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“…The code is obtained from "http://www.cad.zju.edu.cn/home/dengcai/Data/GNMF.html". 7) Local Neighborhood Weights regularized NMF (W-NMF in short) [48] is a graph based NMF method. The main contribution of this method is that it integrates the spectral and spatial information when constructing the weighted graph.…”

Section: )mentioning

confidence: 99%

Structured Sparse Method for Hyperspectral Unmixing

Zhu

Wang

Xiang

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

223

136

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Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.

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Section: )mentioning

confidence: 99%

Structured Sparse Method for Hyperspectral Unmixing

Zhu

Wang

Xiang

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

223

136

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers have made attempts to use the spatial information between different pixels as prior knowledge to enhance HU [39][40][41][42]. To further improve the performance of the sparse NMF algorithm, the graph-regularized L 1{2´N MF (GLNMF) [42] method was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the sparsity-constrained method has been generalized to − for 0 1, and the sparsity imposed by the regularizers upon the unmixing task has been investigated [36,37], which demonstrated the superiority of the / regularizer over the regularizer. In recent years, researchers have made attempts to use the spatial information between different pixels as prior knowledge to enhance HU [39][40][41][42]. To further improve the performance of the sparse NMF algorithm, the graph-regularized / − (GLNMF) [42] method was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…However, this precondition is violated when abrupt changes appear in transition areas or outliers exist in the spatially neighboring regions, as shown in the close-ups of Figure 2a. Methods such as those proposed in [35,39] impose a smoothness constraint on all the spatially neighboring pixels in the local window. The spatial structure information is not fully explored by these methods since they lose sight of the pixels that are inappropriate for the smoothness constraint.…”

Section: Introductionmentioning

confidence: 99%

“…The 3-D scatter plots of the abundance samples show that the abundance variables dominated by different ground objects (an abundance variable dominated by a kind of ground object means that the pixel corresponding to this abundance variable is mainly composed of the ground object) have their own dominant regions and they are dispersed in a convex, which indicates that the abundance variables corresponding to different kinds of materials should be separate and only have a faint correlation. The method proposed in [39] only adds the smoothness constraint to NMF, which may lead to over-smooth results with the increase in iteration times. A constraint to exploit the dispersed characteristic can prevent these undesirable results by pulling the variables toward their own dominant region through minimizing the correlation between any two of the abundance variables.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF

Liu

Zhang

2016

Remote Sensing

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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. In this paper, two characteristics of the abundance variables, namely, the local spatial structural feature and the statistical distribution, are incorporated into nonnegative matrix factorization (NMF) to alleviate the non-convex problem of NMF and enhance the hyperspectral unmixing accuracy. An adaptive local neighborhood weight constraint is proposed for the abundance matrix by taking advantage of the spatial-spectral information of the HSI. The spectral information is utilized to calculate the similarities between pixels, which are taken as the measurement of the smoothness levels. Furthermore, because abrupt changes may appear in transition areas or outliers may exist in spatially neighboring regions, any inappropriate smoothness constraint on these pixels is removed, which can better express the local smoothness characteristic of the abundance variables. In addition, a separation constraint is used to prevent the result from over-smoothing, preserving the inner diversity of the same kind of material. Extensive experiments were carried out on both simulated and real HSIs, confirming the effectiveness of the proposed approach.

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A group interest-based collaborative filtering algorithm for multimedia information

Wang

2017

Multimed Tools Appl

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Enhancing Spectral Unmixing by Local Neighborhood Weights

Cited by 59 publications

References 22 publications

Structured Sparse Method for Hyperspectral Unmixing

Structured Sparse Method for Hyperspectral Unmixing

Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF

A group interest-based collaborative filtering algorithm for multimedia information

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