| DOI: | 10.1016/j.jag.2014.04.004
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| Scopus记录号: | 2-s2.0-84904747641
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| 论文题名: | Remote sensing image denoising application by generalized morphological component analysis |
| 作者: | Yu C; , Chen X
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| 刊名: | International Journal of Applied Earth Observation and Geoinformation
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| ISSN: | 15698432
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| 出版年: | 2014
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| 卷: | 33, 期:1 | | 起始页码: | 83
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| 结束页码: | 97
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| 语种: | 英语
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| 英文关键词: | Analysis
; Blind source separation
; Generalized morphological component
; Iterative thresholding strategy
; Quantitative assessment
; Remote sensing image denoising
; Visual effect
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| Scopus关键词: | algorithm
; complexity
; image analysis
; mathematical analysis
; quantitative analysis
; remote sensing
; signal-to-noise ratio
; visual analysis
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| 英文摘要: | In this paper, we introduced a remote sensing image denoising method based on generalized morpho-logical component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect. © 2014 Elsevier B.V. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/79667
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| Appears in Collections: | 气候变化事实与影响
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作者单位: | School of Information Science and Technology, Fudan University, Shanghai 200433, China
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| Recommended Citation: | |
Yu C,, Chen X. Remote sensing image denoising application by generalized morphological component analysis[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,33(1)
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