Semisupervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images

Zhang, Hongyan; Pižurica, Aleksandra

doi:10.1109/jstars.2019.2895508

Cited by 27 publications

(28 citation statements)

References 35 publications

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“…As the SSC model calculates sparse coefficients individually and independently for each input data point, the clustering performance is sensitive to noise. In order to solve this problem, various extensions have been proposed with the aim to encode the spatial dependencies among the neighbouring data points in hyperspectral data, and obtain thereby more accurate similarity matrices and improved clustering results [18][19][20][21][22][23][24][25]. Guo et al [18,19] focus on the clustering of 1-D drill hole hyperspectral data and regularize the coefficients of neighbouring data points in depth to be similar by a 1 norm based smoothing regularization.…”

Section: Introductionmentioning

confidence: 99%

“…The spectral-spatial SSC method of Reference [22] integrates an 2 spatial regularizer with the SSC model (L2-SSC), to penalize abrupt differences between the coefficients of nearby pixels. In Reference [23,25], an 1,2 norm constraint on the coefficients of pixels in each local region was incorporated in the SSC model. Based on the collaborative representation with an 2 norm constraint on the coefficients, a novel model with a locally adaptive dictionary was proposed in Reference [24].…”

Section: Introductionmentioning

confidence: 99%

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Sketch-Based Subspace Clustering of Hyperspectral Images

Zhang

Pižurica

2020

Remote Sensing

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Sparse subspace clustering (SSC) techniques provide the state-of-the-art in clustering of hyperspectral images (HSIs). However, their computational complexity hinders their applicability to large-scale HSIs. In this paper, we propose a large-scale SSC-based method, which can effectively process large HSIs while also achieving improved clustering accuracy compared to the current SSC methods. We build our approach based on an emerging concept of sketched subspace clustering, which was to our knowledge not explored at all in hyperspectral imaging yet. Moreover, there are only scarce results on any large-scale SSC approaches for HSI. We show that a direct application of sketched SSC does not provide a satisfactory performance on HSIs but it does provide an excellent basis for an effective and elegant method that we build by extending this approach with a spatial prior and deriving the corresponding solver. In particular, a random matrix constructed by the Johnson-Lindenstrauss transform is first used to sketch the self-representation dictionary as a compact dictionary, which significantly reduces the number of sparse coefficients to be solved, thereby reducing the overall complexity. In order to alleviate the effect of noise and within-class spectral variations of HSIs, we employ a total variation constraint on the coefficient matrix, which accounts for the spatial dependencies among the neighbouring pixels. We derive an efficient solver for the resulting optimization problem, and we theoretically prove its convergence property under mild conditions. The experimental results on real HSIs show a notable improvement in comparison with the traditional SSC-based methods and the state-of-the-art methods for clustering of large-scale images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sketch-Based Subspace Clustering of Hyperspectral Images

Zhang

Pižurica

2020

Remote Sensing

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“…In recent years, sparse subspace clustering (SSC) [1] method has achieved the state-of-the-art performance in HSI clustering [2][3][4][5][6][7]. SSC is based on a self-representation model which employs the input data as a dictionary.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the noise sensitivity, some extensions of SSC have been proposed by introducing different spatial constraints [2][3][4][5][6][7]. A smoothing strategy was introduced in [2] by minimizing the coefficient difference between the central pixel and the mean of pixels in a local square window.…”

Section: Introductionmentioning

confidence: 99%

“…A 2 norm based spatial regularization was employed in [4] to smooth the coefficients in the small local region. In [5,7], a joint sparsity constraint with 1,2 norm was applied to the coefficient of each super-pixel. The work in [6] exploited the collaborative representation with a 2 norm and proposed a total variation (TV) regularized model with a locally adaptive dictionary.…”

Section: Introductionmentioning

confidence: 99%

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Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images

Zhang

Pižurica

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Sparse subspace clustering (SSC) has achieved the stateof-the-art performance in the clustering of hyperspectral images (HSIs). However, the high computational complexity and sensitivity to noise limit its clustering performance. In this paper, we propose a scalable SSC method for the large-scale HSIs, which significantly accelerates the clustering speed of SSC without sacrificing clustering accuracy. A small landmark dictionary is first generated by applying k-means to the original data, which results in the significant reduction of the number of optimization variables in terms of sparse matrix. In addition, we incorporate spatial regularization based on total variation (TV) and improve this way strongly robustness to noise. A landmark-based spectral clustering method is applied to the obtained sparse matrix, which further improves the clustering speed. Experimental results on two real HSIs demonstrate the effectiveness of the proposed method and the superior performance compared to both traditional SSC-based methods and the related largescale clustering methods.

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Dictionary learning for clustering on hyperspectral images

Bruton

Wang

2020

SIViP

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Semisupervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images

Cited by 27 publications

References 35 publications

Sketch-Based Subspace Clustering of Hyperspectral Images

Sketch-Based Subspace Clustering of Hyperspectral Images

Landmark-Based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images

Dictionary learning for clustering on hyperspectral images

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