Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2019, Vol. 34 Issue (4): 793-798    DOI: 10.11873/j.issn.1004-0323.2019.4.0793
    
Remote Sensing Image of Mao'ergai Denoising based on Structured Dictionary Learning
Zhentao Qin1(),Ru Yang2
1. School of Mathematics and Computer Science, Panzhihua College, Panzhihua, 617000, China
2. School of Civil and Architecture Engineering, Panzhihua College, Panzhihua, 617000, China
Download:  HTML  PDF (5600KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

The noise analysis, evaluation and denoising of remote sensing image are the focus of RSI processing. In order to improve the denoising ability of remote sensing image, presents a new structured dictionary-based method for multispectral image denoising based on cluster. This method incorporates both the locality of spatial and the correlation across spectrum of multispectral image. Remote sensing image was divided into different groups by clustering, and sparse representation coefficients of spatial and spectral and dictionary is obtained according to the dictionary learning algorithm. After threshold processing, the similar blocks are averaged and realized with multispectral remote sensing image denoising. This algorithm is applied to the denoising of remote sensing image of typical vegetation and soil types in the upper reaches of Minjiang river- Maoergai experimental area. Compared with the band-wise K-SVD algorithm, the PSNR of this algorithm can be improved by about 7.6%, with better visual effect.

Key words:  Remote sensing image      Structured dictionary learning      Denoising      Cluster     
Received:  11 December 2018      Published:  16 October 2019
ZTFLH:  TP751  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Zhentao Qin
Ru Yang

Cite this article: 

Zhentao Qin,Ru Yang. Remote Sensing Image of Mao'ergai Denoising based on Structured Dictionary Learning. Remote Sensing Technology and Application, 2019, 34(4): 793-798.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.4.0793     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I4/793

Fig.1  Location map of the study area (2007 TM321 true-color image)
Fig.2  The pixels of a hyperspectral image partitioned into different image patches
Fig.3  The denoising result of various denoising algorithms
加噪图像固定k=5,噪声标准差δ
0.100.150.200.30
含噪图像14.3113.0311.739.31
Bw KSVD20.3919.5718.8017.38
Bw BM3D22.8022.2421.6920.69
Integral KSVD21.3420.4219.4117.80
3DNLM20.6620.3119.9317.72
PARAFAC14.6814.7314.7414.73
Zhao的算法20.9920.4819.9519.01
本文算法22.8922.2521.7020.86
Table 2  Comparison of denoising performance of different algorithms(K=4,δ=0.10,0.15,0.20,0.30)(dB)
方法BM3DIntegral KSVD3DNLMZhao算法PARAFAC本文算法
时间2.0430.21764.257.794.2025.12
Table 3  Comparison of denoising performance time
Fig.4  The comparison map of denoising effect of various denoising algorithms
1 XuS, ZhouY, XiangH, et al. Remote Sensing Image Denoising Using Patch Grouping-based Nonlocal Means Algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2017:14(12):2275-2279.
2 WangR. Combining Interior and Exterior Characteristics For Remote Sensing Image Denoising[J]. Journal of Applied Remote Sensing, 2016, 10(2):025016. .
doi: 10.1117/1.JRS.10/025016
3 HuangF, LanB, TaoJ, et al. A Parallel Nonlocal Means Algorithm for Remote Sensing Image Denoising on an Intel Xeon Phi Platform[J]. IEEE Access, 2017:1-1. .
doi: 10.1109/ACCESS.2017.2696362
4 WangXianghai, ZhangHongwei, LiFang. A PDE-based Hybrid Model for De-noising Remote Sensing Image with Gaussian and Salt-pepper Noise [J]. Acta Geodaetica et Cartographica Sinica, 2010(3): 283-288.
4 王相海,张洪为,李放. 遥感图像高斯与椒盐噪声的PDE混合去噪模型研究[J]. 测绘学报, 2010(3): 283-288.
5 AharonM, EladM, BrucksteinA. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
6 DabovK, FoiA, KatkovnikV, et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering[J]. IEEE Transactions on Image Processing, 2007, 16(8):2080-2095.
7 ManjónJ, CoupéP, Martí-BonmatíL, et al. Adaptive Non-Local Means Denoising of MR Images with Spatially Varying Noise Levels[J]. Journal of Magnetic Resonance Imaging,2010, 1(31): 192-203.
8 LiuX F, BourennaneS, FossatiC. Denoising of Hyperspectral Images Using the Parafac Model and Statistical PerforMance Analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 10(50): 3717-3724.
9 ZhaoY, YangJ. Hyperspectral Image Denoising via Sparse Representation and Low-rank Constraint[J]. IEEE Transactions on Geoscience and Remote Sensing. 2015, 1(51): 296-308.
10 ZhouXia, ZhuoZhihong, YaoMin. Hyperspectral Remote Image Denoising based on Multi-task Nonnegative Dictionary Learning [J]. Control Engineering of China, 2017,24(12): 2544-2548.
10 周霞,卓志宏,姚敏. 多任务非负字典学习的高光谱遥感图像去噪[J]. 控制工程,2017,24(12):2544-2548.
11 WangXiaoyan, ChiTianhe. A Satellite Image Denoising Method based on MPSO and Dictionary Learning [J]. Computer Engineering and Science,2017,39(9):1675-1681.
11 王晓燕,池天河. 结合变异粒子群和字典学习的遥感影像去噪[J]. 计算机工程与科学,2017,39(9):1675-1681.
12 SongXiangfa, JiaoLicheng. Classification of Hyperspectral Remote Sensing Image based on Sparse Representation and Spectral Information[J]. Journal of Electronics & Information Technology, 2012(2): 268-272.
12 宋相法,焦李成. 基于稀疏表示及光谱信息的高光谱遥感图像分类[J]. 电子与信息学报, 2012(2): 268-272.
13 SongLin, ChengYongmei, ZhaoYongqiang. Hyper-spectromicroscope Classification based on Sparse Representation Model and Auto-regressive Model [J]. Acta Optica Sinica, 2012(3): 322-328.
13 宋琳,程咏梅,赵永强. 基于稀疏表示模型和自回归模型的高光谱分类[J]. 光学学报, 2012(3): 322-328.
14 XiaQin, XingShuai ,MaDongyang,et al. An Improved K-SVD-based Denoising Method for Remote Sensing Satellite Images [J]. Journal of Remote Sensing,2016,20(3):441-449.
14 夏琴,邢帅,马东洋,莫德林,等. 遥感卫星影像K-SVD稀疏表示去噪[J]. 遥感学报,2016,20(3):441-449.
15 ZhangCongmei, SunQuansen, WangChao,et al. Remote Sensing Image De-noising via Sparse Representation based on Non-local Self-similarity [J]. Remote Sensing Technology and Application, 2016,31(4):739-747.
15 张从梅,孙权森,王超,等. 基于非局部自相似性的遥感图像稀疏去噪算法[J]. 遥感技术与应用,2016, 31(4): 739-747.
16 PengY, MengD, XuZ, et al. Decomposable Nonlocal Tensor Dictionary Learning form Multispectral Image Denoising[C]∥ 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
17 CichockiA, MandicD P, PhanA H, et al. Tensor Decompositions for Signal Processing Applications from Two-way to Multiway Component Analysis[J]. IEEE Signal Processing Magazine, 2014, 32(2):145-163.
18 ArthurD. k-means++ : The Advantages of Careful Seeding[C]∥Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007.
No Suggested Reading articles found!