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Manifold regularization for sparse unmixing of hyperspectral images

View Article: PubMed Central - PubMed

ABSTRACT

Background: Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression, ignore the intrinsic geometric structure in the hyperspectral data.

Results: In this paper, we propose a novel model, called manifold regularized collaborative sparse regression, by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model.

Conclusions: Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

No MeSH data available.


Seven randomly selected endmember spectra to generate SI-2 and the corresponding simulated images with band 50. a Seven endmember spectra and b synthetic image with band 50
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Fig2: Seven randomly selected endmember spectra to generate SI-2 and the corresponding simulated images with band 50. a Seven endmember spectra and b synthetic image with band 50

Mentions: This synthetic image is to simulate a scene with land covers arranged in discrete patches, and was designed in Miao and Qi (2007). In the image, seven endmembers, randomly selected from the spectral library and as shown in Fig. 2a, are used to produce a hyperspectral image with pixels. The image is first divided into several regions with each initialized with one of the seven endmember spectra, and then a spatial low-pass filter is used to generate the mixed pixels. In order to model the scene without pure pixels, all of the pixels whose abundances are larger than 0.8 are replaced with a mixture of all endmembers with equal abundances. By this way, the produced fractional abundances naturally satisfy the LMM with the nonnegative and sum-to-one constraints.Fig. 2


Manifold regularization for sparse unmixing of hyperspectral images
Seven randomly selected endmember spectra to generate SI-2 and the corresponding simulated images with band 50. a Seven endmember spectra and b synthetic image with band 50
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121117&req=5

Fig2: Seven randomly selected endmember spectra to generate SI-2 and the corresponding simulated images with band 50. a Seven endmember spectra and b synthetic image with band 50
Mentions: This synthetic image is to simulate a scene with land covers arranged in discrete patches, and was designed in Miao and Qi (2007). In the image, seven endmembers, randomly selected from the spectral library and as shown in Fig. 2a, are used to produce a hyperspectral image with pixels. The image is first divided into several regions with each initialized with one of the seven endmember spectra, and then a spatial low-pass filter is used to generate the mixed pixels. In order to model the scene without pure pixels, all of the pixels whose abundances are larger than 0.8 are replaced with a mixture of all endmembers with equal abundances. By this way, the produced fractional abundances naturally satisfy the LMM with the nonnegative and sum-to-one constraints.Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression, ignore the intrinsic geometric structure in the hyperspectral data.

Results: In this paper, we propose a novel model, called manifold regularized collaborative sparse regression, by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model.

Conclusions: Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

No MeSH data available.