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| Low-rank Structure Based Hyperspectral Compression Representation |
| TANG Zhongqi①② FU Guangyuan① CHEN Jin③ ZHANG Li② |
①(Xi’an Institute of High-Tech, Xi’an 710025, China)
②(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)
③(Beijing Institute of Remote Sensing Information, Beijing 100192, China) |
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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.
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Received: 30 July 2015
Published: 26 February 2016
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| Fund: 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) |
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Corresponding Authors:
TANG Zhongqi
E-mail: tangzq12@mails.tsinghua.edu.cn
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