Low-rank Structure Based Hyperspectral Compression Representation

TANG Zhongqi; FU Guangyuan; CHEN Jin; ZHANG Li

doi:10.11999/JEIT150906

Volume 38 Issue 5

May 2016

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TANG Zhongqi, FU Guangyuan, CHEN Jin, ZHANG Li. Low-rank Structure Based Hyperspectral Compression Representation[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1085-1091. doi: 10.11999/JEIT150906

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TANG Zhongqi, FU Guangyuan, CHEN Jin, ZHANG Li. Low-rank Structure Based Hyperspectral Compression Representation[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1085-1091. doi: 10.11999/JEIT150906

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Low-rank Structure Based Hyperspectral Compression Representation

doi: 10.11999/JEIT150906

1.
2.
(Xi&rsquo

Funds:

The National Natural Science Foundation of China (61132007, 61202332, 61503405), The National Natural Science Foundation for Young Scientists of China (61403397), China Postdoctoral Science Foundation (2012M521905), Natural Science Foundation of Shaanxi Province, China (2015JM6313)

Received Date: 2015-07-30
Rev Recd Date: 2015-12-31
Publish Date: 2016-05-19

Abstract

Abstract

A method which makes use of structure information abstracted from hyperspectral data via low-rank matrix recovery for hyperspectral image classification is proposed in this paper. The principle of maximizing structure information based on Structural Similarity Index Measurement (SSIM) is proposed to restrain the process of matrix recovery as well, which facilitates the separation of the signal and the noise. The experiments show that the proposed algorithm can effectively eliminate the non-linear noise in hyperspectral image and abstract the low-rank characteristics of hyperspectral image, which achieves better performance in classification.
- Hyperspectral image classification,
- Compression representation,
- Low-rank matrix recovery

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References(20)

References

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