Hyperspectral data unmixing with graph-based regularization

Ammanouil, Rita; Ferrari, A.; Richard, Cédric

doi:10.1109/whispers.2015.8075377

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…These methods ignore the underlying structure of the hyperspectral images. To take advantage of this property, some graph-based methods are proposed [28,29], which employ the graph topology and sparse group lasso regularization.…”

Section: Introductionmentioning

confidence: 99%

“…There are some works related to us (e.g., [4,29]). In [4], Wang et al introduced a double reweighted sparse unmixing and TV (DRSU-TV) algorithm for hyperspectral unmixing.…”

Section: Introductionmentioning

confidence: 99%

“…The difference is that they employ the total variation regularization which utilizes local neighbourhood pixels on the abundances rather than graph regularization which can exploit the structures hidden in the hyperspectral data and allow connecting a pixel with as many other pixels in the image as long as they are similar. In [29], the authors incorporated a graph-based total variation framework within the unmixing problem, which takes the strength of graph TV regularization and l 2,1 -norm regularization. Unlike [29], in which the graph TV regularization is imposed on the reconstructed spectra, we exploit the graph Laplacian directly on abundances.…”

Section: Introductionmentioning

confidence: 99%

“…In [29], the authors incorporated a graph-based total variation framework within the unmixing problem, which takes the strength of graph TV regularization and l 2,1 -norm regularization. Unlike [29], in which the graph TV regularization is imposed on the reconstructed spectra, we exploit the graph Laplacian directly on abundances. In addition, we adopt a double reweighted l 1 -norm rather than l 2,1 -norm regularization employed in work [29].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike [29], in which the graph TV regularization is imposed on the reconstructed spectra, we exploit the graph Laplacian directly on abundances. In addition, we adopt a double reweighted l 1 -norm rather than l 2,1 -norm regularization employed in work [29].…”

Section: Introductionmentioning

confidence: 99%

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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

“…There are some works related to us (e.g., [4,29]). In [4], Wang et al introduced a double reweighted sparse unmixing and TV (DRSU-TV) algorithm for hyperspectral unmixing.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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GPU Implementation of Graph-Regularized Sparse Unmixing With Superpixel Structures

Zeng

Chen

Movania

et al. 2023

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

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To enhance spectral unmixing performance, a large number of algorithms have simultaneously investigated spatial and spectral information in hyperspectral images. However, sophisticated algorithms with high computational complexity can be very time-consuming when a large amount of data are involved in processing hyperspectral images. In this paper, we first introduce a group sparse graph-regularized unmixing method with superpixel structure, to promote piece-wise consistency of abundances and reduce computational burden. Segmenting the image into several non-overlapped superpixels also enables to decompose the unmixing problem into uncoupled subproblems that can be processed in parallel. An implementation for the proposed algorithm on graphics processing units (GPUs) is then developed based on the NVIDIA Compute Unified Device Architecture (CUDA) framework. The proposed scheme achieves parallelism at both the intra-superpixel and inter-superpixel levels, where multiple concurrent streams have been used to enable multiple kernels to execute on the device simultaneously. Simulation results with a series of experiments demonstrate advantages of the proposed algorithm. The performance of the GPU implementation also illustrates that parallel scheme largely expedites the implementation.

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