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Remote Sens. 2017, 9(4), 335; doi:10.3390/rs9040335

Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation

1
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, and the Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430079, China
2
College of Surveying and Geoinformatics, Tongji University, Shanghai 200000, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz, Magaly Koch and Prasad S. Thenkabail
Received: 24 January 2017 / Revised: 23 March 2017 / Accepted: 28 March 2017 / Published: 1 April 2017
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Abstract

Hyperspectral image (HSI) clustering is generally a challenging task because of the complex spectral-spatial structure. Based on the assumption that all the pixels are sampled from the union of subspaces, recent works have introduced a robust technique—the sparse subspace clustering (SSC) algorithm and its enhanced versions (SSC models incorporating spatial information)—to cluster HSIs, achieving excellent performances. However, these methods are all based on the linear representation model, which conflicts with the well-known nonlinear structure of HSIs and limits their performance to a large degree. In this paper, to overcome these obstacles, we present a new kernel sparse subspace clustering algorithm with a spatial max pooling operation (KSSC-SMP) for hyperspectral remote sensing data interpretation. The proposed approach maps the feature points into a much higher dimensional kernel space to extend the linear sparse subspace clustering model to nonlinear manifolds, which can better fit the complex nonlinear structure of HSIs. With the help of the kernel sparse representation, a more accurate representation coefficient matrix can be obtained. A spatial max pooling operation is utilized for the representation coefficients to generate more discriminant features by integrating the spatial-contextual information, which is essential for the accurate modeling of HSIs. This paper is an extension of our previous conference paper, and a number of enhancements are put forward. The proposed algorithm was evaluated on two well-known hyperspectral data sets—the Salinas image and the University of Pavia image—and the experimental results clearly indicate that the newly developed KSSC-SMP algorithm can obtain very competitive clustering results for HSIs, outperforming the current state-of-the-art clustering methods. View Full-Text
Keywords: hyperspectral images; subspace clustering; nonlinear techniques; kernels; spatial max pooling hyperspectral images; subspace clustering; nonlinear techniques; kernels; spatial max pooling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhai, H.; Zhang, H.; Xu, X.; Zhang, L.; Li, P. Kernel Sparse Subspace Clustering with a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation. Remote Sens. 2017, 9, 335.

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