Hyperspectral Unmixing Using Double Reweighted Sparse Regression and Total Variation

Wang, Rui; Li, Heng-Chao; Pižurica, Aleksandra; Li, Jun; Plaza, Antonio; Emery, William J.

doi:10.1109/lgrs.2017.2700542

Cited by 97 publications

(48 citation statements)

References 15 publications

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“…To further improve the estimation performance over the l 1 -norm and promote the sparsity of the solution, some other regularizers, such as l p -norm regularizer, collaborative sparse regularizer [22], reweighted collaborative sparse regularizer [23] are proposed to solve unmixing problems. In this paper, we propose to use a weighted formulation of l 1 minimization to improve the estimation performance over the l 1 -norm, inspired by [4,44,45]. Considering the fact that a hyperspectral image usually includes fewer endmembers compared with the overcomplete spectral library and the abundance map is inherently sparse, we employ the double reweighted l 1 sparse constraint which defined as…”

Section: Double Reweighted L 1 Sparse Priormentioning

confidence: 99%

“…Mixed pixels are commonly present in hyperspectral data due to the low spatial resolution of the sensors and the complexity of the terrain. The occurrence of mixed pixels severely degrades the application of hyperspectral data [4]. Thus, hyperspectral unmixing (HU) is an important process for hyperspectral data exploitation.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate this disadvantage, many methods have been proposed by adding some extra constraints under the NMF framework. For example, spatial and spectral regularization is considered in [4,17], the sparsity-constrained of abundances is considered in [18], and the manifold structure of the hyperspectral data is considered in [19,20]. All these methods are unsupervised methods, i.e., both the endmember signatures and the abundances are unknown.…”

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%

“…In [4], Wang et al introduced a double reweighted sparse unmixing and TV (DRSU-TV) algorithm for hyperspectral unmixing. 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.…”

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: Double Reweighted L 1 Sparse Priormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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%

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