Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

İnce, Taner

doi:10.1109/lgrs.2020.3027055

Cited by 33 publications

(19 citation statements)

References 28 publications

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“…Superpixel segmentation divides the HSI into perceptually disconnected homogeneous regions with similar properties, each of which is called a superpixel. The value of superpixel segmentation in the unmixing processing of HSI has been recognized in the literature [30], [34], [35], [40]. Thus, the obtained homogeneous and uniform regions based superpixel segmentation method is adopted in this paper.…”

Section: A Hsi Representation Using Superpixel Segmentationmentioning

confidence: 99%

“…It should be noted that the convergence of Algorithm 1 is difficult to justify. The inner and outer loop iterative method is adopted to solve the optimization problem of the model [23], [34]. The inner loop corresponds to the update of the abundance coefficients via the ADMM and the outer loop corresponds to the update of the spatial weight.…”

Section: Selection Of Weighting Coefficientsmentioning

confidence: 99%

“…[24], and spatially distributed in the form of a square area, the background mixture is composed of five endmembers with fractional abundances 0.1149, 0.0742, 0.2003, 0.2055, and 0.4051. In the second data set (SD2), nine spectral signatures from the spectral library 𝐷 are selected randomly as active endmembers, and the corresponding fractional abundance maps with the spatial size of 100 × 100 pixels are adopted, which were sampled according to a Dirichlet distribution centered on the Gaussian random field [34]. These two simulated data are contaminated by Gaussian noise of the signal-to-noise ratio (SNR), i.e., 20dB, 30dB, and 40dB.…”

Section: A Simulated Data Experimentsmentioning

confidence: 99%

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Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Jia

Pan

et al. 2022

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

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Sparse unmixing with a semi-supervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit the performance of sparse unmixing. In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted low-rank representation unmixing (SBWCRLRU). In the method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes the rank of the abundance matrix to promote the spatial consistency of the image. Meanwhile, superpixel segmentation is adopted to cluster the pixels into different spatial homogeneous regions to further improve the unmixing performance. Extensive experiments results conducted on both synthetic and real data demonstrate the effectiveness of the proposed SBWCRLRU. It can not only improve the performance of hyperspectral unmixing but also outperform the existing sparse unmixing approaches.

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Section: A Hsi Representation Using Superpixel Segmentationmentioning

confidence: 99%

Section: Selection Of Weighting Coefficientsmentioning

confidence: 99%

Section: A Simulated Data Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Jia

Pan

et al. 2022

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

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show abstract

“…Previous studies have shown that the integration of spatial information, whether in unmixing procedure itself or as a preprocessing step, contributes to improving the accuracy of the abundance estimation [36], [37]. The weighting factor related to the spatial location is introduced into the spectral unmixing model, which is a simple way to exploit spatial information [30].…”

Section: A Formulation Of Proposed Rdsrsu Modelmentioning

confidence: 99%

Robust Double Spatial Regularization Sparse Hyperspectral Unmixing

Li¹,

Zhang²,

Deng³

et al. 2021

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

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With the help of endmember spectral library, sparse unmixing techniques have been successfully applied to hyperspectral image interpretation. The inclusion of spatial information in the sparse unmixing significantly improves the resulting fractional abundances. However, most existing spatial sparse unmixing algorithms are sensitive to noise and produce unstable solutions. To alleviate this drawback, a new robust double spatial regularization sparse unmixing (RDSRSU) method is proposed, which simultaneously exploits the spatial structure information from hyperspectral images and estimated abundance maps to mitigate the negative influence of noise on unmixing, so as to achieve robust sparse unmixing. To this end, a pre-calculated spatial weighting factor is introduced to maintain the original spatial information of the hyperspectral image. Meanwhile, the total variation spatial regularizer is used to capture the piecewise smooth structure of each abundance map. The experimental results, conducted by two sets of simulated data, as well as Cuprite and Mangrove real hyperspectral data, uncover that the proposed RDSRSU algorithm can offer better anti-noise ability and obtain more accurate results over those gave by other advanced sparse unmixing algorithms.

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“…Double reweighted sparse unmixing (DRSU) [5] and double reweighted sparse unmixing and TV (DRSU-TV) [6] employ two reweighting factor in their solutions. Since homogeneous regions in a hyperspectral image have the same spectral characteristics, segmentation based approaches are investigated in many studies for classification and unmixing problems [7]- [12]. Ince et.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

İnce¹,

Dundar²,

Kacmaz³

et al. 2022

Preprint

Self Cite

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We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

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Superpixel-Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing

Cited by 33 publications

References 28 publications

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Robust Double Spatial Regularization Sparse Hyperspectral Unmixing

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

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