Dimensionality Reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized Auto-encoder

WANG Xuesong; KONG Yi; CHENG Yuhu

doi:10.1049/cje.2017.07.012

Volume 26 Issue 6

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WANG Xuesong, KONG Yi, CHENG Yuhu, “Dimensionality Reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized Auto-encoder,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1233-1238, 2017, doi: 10.1049/cje.2017.07.012

Citation:

WANG Xuesong, KONG Yi, CHENG Yuhu, “Dimensionality Reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized Auto-encoder,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1233-1238, 2017, doi: 10.1049/cje.2017.07.012

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Dimensionality Reduction for Hyperspectral Data Based on Sample-Dependent Repulsion Graph Regularized Auto-encoder

doi: 10.1049/cje.2017.07.012

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: This work is supported by National Natural Science Foundation of China (No.61273143, No.61472424).

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Corresponding author: CHENG Yuhu (Corresponding author) received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. He is currently a professor in the School of Information and Control Engineering, China University of Mining and Technology.(Email:chengyuhu@163.com)
Received Date: 2015-08-12
Rev Recd Date: 2015-09-16
Publish Date: 2017-11-10

Abstract

Abstract

To achieve high classification accuracy of hyperspectral data, a dimensionality reduction algorithm called Sample-dependent repulsion graph regularized auto-encoder (SRGAE) is proposed. Based on the sample-dependent graph, by applying the repulsion force to the samples from different classes but nearby, a sampledependent repulsion graph is built to make the samples from the same class will be projected to samples that are close-by and the samples from different classes will be projected to samples that are far away. The sampledependent repulsion graph can avoid the neighborhood parameter selection problem existing in the nearest neighborhood graph. By integrating advantages of deep learning and graph regularization technique, the SRGAE can maintain the learned deep features are consistent with the inherent manifold structure of the original hyperspectral data. Experimental results on two real hyperspectral data show that, when compared with some popular dimensionality reduction algorithms, the proposed SRGAE can yield higher classification accuracy.
- Hyperspectral data,
- Dimensionality reduction,
- Sample-dependent repulsion graph,
- Regularized,
- Auto-encoder

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References(18)

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