Mr. Yan Li Profile

Yan Li

at Nanchang Institute of Technology

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Publications (2)

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Proceedings Article | 14 August 2019 Paper

Hyperspectral unmixing using double reweighted collaborative sparse regression

Yan Li, Shengqian Wang

Proceedings Volume 11179, 1117933 (2019) https://doi.org/10.1117/12.2540096

KEYWORDS: Remote sensing, Hyperspectral imaging, Optimization (mathematics), Computer simulations, Image processing

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An important technique in hyperspectral unmixing is collaborative sparse regression. It improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Now it is well known that introducing weighted factors to enforce sparseness becomes a necessary process in sparse unmixing methods. In this paper, considering the desirable performance of reweighted minimization, a double reweighted collaborative sparse regression (DR-CLSUnSAL) has been utilized. The proposed method enhances the sparsity of abundance factions in both the spectral sparsity (column sparsity of the fractional abundances in the sense) and the spatial sparsity (row sparsity of the fractional abundances in the sense). Then the optimization problem was simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results with simulated data sets generated by randomly extracting from the United State Geological Survey(USGS) library demonstrate that the proposed method is an effective and accurate sparse unmixing algorithm compared with other advanced hyperspectral unmixing methods.

Proceedings Article | 21 July 2017 Paper

Sparse hyperspectral unmixing combined L1/2 norm and reweighted total variation regularization

Yan Li

Proceedings Volume 10420, 1042046 (2017) https://doi.org/10.1117/12.2282140

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Sparse regression aims at estimating the fractional abundances of pure endmembers based on the assumption that each mixed pixel in the hyperspectral image can be expressed in the form of linear combinations of a number of known and pure endmembers. And total variation spatial regularization for sparse unmixing has been proposed with incorporating spatial information. In this paper, considering the desirable performance of reweighted minimization and owing to the L_1/2 norm is an alternative regularizer which is much easier to solved than L₀ regularizer and has better sparsity and robustness than L₁ regularizer, a sparse regression combined L_1/2 norm and reweighted total variation regularization has been utilized. Then the unconvex optimization problem is simply solved by the variable splitting and augmented Lagrangian algorithm. Our experimental results with simulated data sets and real hyperspectral data sets demonstrate that the proposed method is an effective and accurate spectral unmixing algorithm for hyperspectral regression.

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