Limits...
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.


Related in: MedlinePlus

The recovered fractional abundance maps of endmember 1, 3, 5 and 7 for SI-2 by the MCSUnADMM algorithm. a Endmember 1, b Endmember 3, c Endmember 5, d Endmember 7
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5121117&req=5

Fig10: The recovered fractional abundance maps of endmember 1, 3, 5 and 7 for SI-2 by the MCSUnADMM algorithm. a Endmember 1, b Endmember 3, c Endmember 5, d Endmember 7

Mentions: Additionally, the estimated fractional abundances obtained by the three algorithms, along with the ground-truth abundances, are shown in Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13. For space consideration, only the fractional abundance maps of SI-1 with the lowest SNR of 15 dB and the fractional abundance maps of SI-2 with the highest SNR of 45 dB are reported. By visual comparisons of these fractional abundance maps, it can be seen that our proposed MCSUnADMM algorithm based on MCS model outperforms the other two algorithms for SR and CSR models and the incorporated manifold regularization can impose spatial consistency such that the spatially similar pixels have similar abundances as shown in Figs. 5, 6, 7, 8, 9 and 10. From Fig. 7, we can see that the results obtained by the TVSUnSAL algorithm exhibits more spatial homogeneity due to the total variation regularization. However, the proposed MCSUnADMM algorithm can deal with the pixels in the transaction areas, as shown in Fig. 5, since its incorporated manifold regularization has applied different weights (9) for different pixels.Fig. 4


Manifold regularization for sparse unmixing of hyperspectral images
The recovered fractional abundance maps of endmember 1, 3, 5 and 7 for SI-2 by the MCSUnADMM algorithm. a Endmember 1, b Endmember 3, c Endmember 5, d Endmember 7
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig10: The recovered fractional abundance maps of endmember 1, 3, 5 and 7 for SI-2 by the MCSUnADMM algorithm. a Endmember 1, b Endmember 3, c Endmember 5, d Endmember 7
Mentions: Additionally, the estimated fractional abundances obtained by the three algorithms, along with the ground-truth abundances, are shown in Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13. For space consideration, only the fractional abundance maps of SI-1 with the lowest SNR of 15 dB and the fractional abundance maps of SI-2 with the highest SNR of 45 dB are reported. By visual comparisons of these fractional abundance maps, it can be seen that our proposed MCSUnADMM algorithm based on MCS model outperforms the other two algorithms for SR and CSR models and the incorporated manifold regularization can impose spatial consistency such that the spatially similar pixels have similar abundances as shown in Figs. 5, 6, 7, 8, 9 and 10. From Fig. 7, we can see that the results obtained by the TVSUnSAL algorithm exhibits more spatial homogeneity due to the total variation regularization. However, the proposed MCSUnADMM algorithm can deal with the pixels in the transaction areas, as shown in Fig. 5, since its incorporated manifold regularization has applied different weights (9) for different pixels.Fig. 4

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.


Related in: MedlinePlus