Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Zhong, Yanfei; Feng, Ruyi; Zhang, Liangpei

doi:10.1109/jstars.2013.2280063

Cited by 115 publications

(54 citation statements)

References 30 publications

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“…The "reference" column represents the SREs of unmixing results using clean data (not contaminated by the noise) to construct the graph Laplacian matrix. It clearly demonstrates the advantage of pre-denoising before constructing L. Some other denoising methods could also be applied to get better performance, such as non-local total variation, BM3D, and sparse representation [2,26]. …”

Section: The Graph Laplacian Matrix Lmentioning

confidence: 99%

“…In general, two restrictions are imposed on the fractional abundances X in LMM due to physical constraints [5,21,24,26,33]. On the one hand, we often assume X ij 0 where X ij denotes the (i, j)-th entry of X, termed as the abundance non-negative constraint (ANC).…”

Section: Linear Mixture Model (Lmm)mentioning

confidence: 99%

“…In [25], Zhao et al studied total variation regularization in deblurring and sparse unmixing of hyperspectral images and their method gets better performance than SUnSAL-TV. Taking the non-local spatial information of whole abundance image into account, the non-local mean unmixing method which utilizes the similar patterns and structures in the abundance map is proposed in [26]. Allowing for the low rankness and sparsity property of abundance matrices, Giampouras et al proposed a method which combines the weighted l 1 -norm and the weighted nuclear norm of the abundance matrix as a penalty term [27].…”

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: The Graph Laplacian Matrix Lmentioning

confidence: 99%

Section: Linear Mixture Model (Lmm)mentioning

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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“…With the LMM, it is assumed that the spectra of each mixed pixel are linear combinations of the endmembers contained in the pixel. Despite the fact that it holds only for macroscopic mixture conditions [8,11], it is widely used due to its computational tractability and flexibility in various applications.…”

Section: Introductionmentioning

confidence: 99%

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Rizkinia

Okuda

2017

Remote Sensing

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Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2,1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.

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“…Mei et al proposed a novel spatially and spectrally constrained sparse unmixing algorithm by imposing spatial and spectral constraints in selecting endmembers from a spectral library that consists of image-derived endmembers [38], which can alleviate the influence of spectral variation. Zhong et al proposed a new sparse unmixing algorithm based on non-local means (NLSU) [39], and it introduces a non-local mean regularization term for sparse unmixing through a weighting average for all the pixels in the abundance image, which is adopted to exploit the similar patterns and structures of the abundance. Iordache et al also proposed collaborative SUnSAL (CLSUnSAL) to improve unmixing results by adopting the collaborative sparse regression framework [40], where the sparsity of abundance is characterized by the 2,1 norm and simultaneously imposed on all pixels in the HSI.…”

Section: Introductionmentioning

confidence: 99%

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

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Abstract:Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Cited by 115 publications

References 30 publications

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

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