结合PCA及字典学习的高光谱图像自适应去噪方法

doi:10.11772/j.issn.1001-9081.2016.12.3411

计算机应用 ›› 2016, Vol. 36 ›› Issue (12): 3411-3417.DOI: 10.11772/j.issn.1001-9081.2016.12.3411

结合PCA及字典学习的高光谱图像自适应去噪方法

汪浩然¹, 夏克文¹, 任苗苗^2,3, 李绰¹

1. 河北工业大学电子与信息工程学院, 天津 300401;
2. 中国科学院电子学研究所, 北京 100190;
3. 中国科学院大学, 北京 100190

收稿日期:2016-05-23 修回日期:2016-07-04 出版日期:2016-12-10 发布日期:2016-12-08
通讯作者: 夏克文
作者简介:汪浩然(1990-),男,天津人,硕士研究生,主要研究方向:高光谱图像处理、压缩感知;夏克文(1956-),男,湖南邵阳人,教授,博士,主要研究方向:石油测井数据挖掘、智能天线;任苗苗(1992-),女,河北石家庄人,硕士研究生,主要研究方向:合成孔径雷达图像处理、压缩感知;李绰(1995-),女,河北石家庄人,主要研究方向:粒计算、粗糙集。
基金资助:
国家自然科学基金资助项目（51208168）；天津市自然科学基金资助项目（13JCYBJC37700）；河北省自然科学基金资助项目（E2016202341）；大学生创新创业训练计划项目（河北省重点）（201510080051）。

Adaptive denoising method of hyperspectral remote sensing image based on PCA and dictionary learning

WANG Haoran¹, XIA Kewen¹, REN Miaomiao^2,3, LI Chuo¹

1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;
2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
3. University of Chinese Academy of Sciences, Beijing 100190, China

Received:2016-05-23 Revised:2016-07-04 Online:2016-12-10 Published:2016-12-08
Supported by:
This work is partially supported by he National Natural Science Foundation of China (51208168), the Natural Science Foundation of Tianjin City (13JCYBJC37700), the Natural Science Foundation of Hebei Province (E2016202341), the Hebei Provincial Key Training Programs of Innovation and Entrepreneurship for Undergraduates (201510080051).

摘要/Abstract

摘要： 高光谱图像各波段图像噪声分布复杂，传统去噪方法难以达到理想效果。针对这一问题，在主成分分析（PCA）的基础上，结合噪声估计和字典学习，提出一种新的高光谱去噪方法。首先，对原始高光谱数据进行主成分变换得到一组主成分图像并根据能量比重将其划分为清晰图像组和含噪图像组；然后，根据任一波段图像的信息，利用奇异值分解（SVD）对图像进行噪声估计，再将得到的噪声估计方法与K-SVD字典学习去噪算法结合，提出一种具备自适应噪声估计特性的字典学习去噪算法，并将其应用于信息量较小的含噪图像组进行去噪处理；最后，按各主成分图像对应的信息量比例进行加权融合得到最终的去噪图像。通过对模拟与实际高光谱遥感图像的实验表明，与PCA、PCA-Bish、PCA-Contourlet三种去噪方法相比，所提方法去噪后图像的峰值信噪比（PSNR）可以提升1~3 dB，且具有更多的细节信息和更好的视觉效果。

关键词: 高光谱遥感, 主成分分析, 噪声估计, 奇异值分解, 字典学习

Abstract: The distributed state of noise existing among different bands of hyperspectral remote sensing image is complex, so the traditional denoising methods are hard to achieve the desired effect. In order to solve this problem, based on Principal Component Analysis (PCA), a novel denoising method for hyperspectral data was proposed combining with noise estimation and dictionary learning. Firstly, a group of the principal component images were achieved from the original hyperspectral data by using the PCA transform, which were divided into clear image group and noisy image group according to the corresponding energy. Then, according to any band image from noisy hyperspectral data, the noise standard deviation of the image was estimated via a noise estimation method based on Singular Value Decomposition (SVD). Meanwhile, combining this noise estimation method with denoising method via K-SVD dictionary learning, a new dictionary learning denoising method with adaptive noise estimation characteristics was proposed and applied to denoise those images from noisy image group with low energy where noise mainly existed. Finally, the final denoising image was obtained by weighted fusion according to the corresponding energy of each principal component image. The experimental results on simulated and real hyperspectral remote sensing data show that, compared with PCA, PCA-Bish and PCA-Contourlet, the Peak Signal-to-Noise Ratio (PSNR) of the image denoised by the proposed algorithm is improved by 1-3 dB, and more detailed information and better visual effect of the denoised image by the proposed method are achieved.

Key words: hyperspectral remote sensing, Principal Component Analysis (PCA), noise estimation, Singular Value Decomposition (SVD), dictionary learning

中图分类号:

TP751

汪浩然, 夏克文, 任苗苗, 李绰. 结合PCA及字典学习的高光谱图像自适应去噪方法[J]. 计算机应用, 2016, 36(12): 3411-3417.

WANG Haoran, XIA Kewen, REN Miaomiao, LI Chuo. Adaptive denoising method of hyperspectral remote sensing image based on PCA and dictionary learning[J]. Journal of Computer Applications, 2016, 36(12): 3411-3417.

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结合PCA及字典学习的高光谱图像自适应去噪方法

Adaptive denoising method of hyperspectral remote sensing image based on PCA and dictionary learning

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