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Proceedings Paper

Efficient sparse subspace clustering for polarized hyperspectral images
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Paper Abstract

Hyperspectral images (HSI) have a strong ability in information expression, and sparse subspace clustering (SSC) model for HSI have become very popular in recent years. Due to polarization information has a good performance on the edges and roughness of materials, adding polarization information into HSI clustering can give better results. In this paper, a fast spectral-polarized sparse subspace clustering (FSP-SSC) algorithm combining hyperspectral information and polarized information is presented. Furthermore, a new framework in the manner of sampling-clustering-classifying is used to reduce the computational complexity of the algorithm: firstly, super pixels which are segmented form original images by simple linear iterative clustering (SLIC) are sampled; then the samples are clustered by solving the optimization problem considering both of hyperspectral information and polarized information; after that, we can get the final cluster results by classifying non-sampled super pixels into the clusters based on the sampled super pixels. Some experiments have been performed to demonstrate the accuracy, efficiency and potential capabilities of proposed algorithm.

Paper Details

Date Published: 24 January 2019
PDF: 9 pages
Proc. SPIE 11052, Third International Conference on Photonics and Optical Engineering, 110520Z (24 January 2019); doi: 10.1117/12.2521932
Show Author Affiliations
Zhengyi Chen, Xi'an Jiaotong Univ. (China)
Chunmin Zhang, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 11052:
Third International Conference on Photonics and Optical Engineering
Ailing Tian, Editor(s)

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