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

Sparse subspace clustering (SSC) has achieved the state-of-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 reg-ularization 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 large-scale clustering methods.

[1]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[2]  Georgios B. Giannakis,et al.  Sketched Subspace Clustering , 2017, IEEE Transactions on Signal Processing.

[3]  Liangpei Zhang,et al.  Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation , 2017, Remote. Sens..

[4]  Aleksandra Pizurica,et al.  Joint Sparsity Based Sparse Subspace Clustering for Hyperspectral Images , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).

[5]  Xinlei Chen,et al.  Large Scale Spectral Clustering Via Landmark-Based Sparse Representation , 2015, IEEE Transactions on Cybernetics.

[6]  Hongyan Zhang,et al.  Semisupervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Antonio J. Plaza,et al.  A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery , 2017, IEEE Geoscience and Remote Sensing Letters.

[8]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[9]  Shaoguang Huang,et al.  Semi-supervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images , 2019 .

[10]  Liangpei Zhang,et al.  Total Variation Regularized Collaborative Representation Clustering With a Locally Adaptive Dictionary for Hyperspectral Imagery , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Liangpei Zhang,et al.  Spectral–Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Zhang Yi,et al.  Scalable Sparse Subspace Clustering , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  R. Vidal,et al.  Sparse Subspace Clustering: Algorithm, Theory, and Applications. , 2013, IEEE transactions on pattern analysis and machine intelligence.

[14]  Wei Liu,et al.  Large Graph Construction for Scalable Semi-Supervised Learning , 2010, ICML.