Fast variational detection of stripe noise based on interval sampling

doi:10.6046/zrzyyg.2022161

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Abstract

This study proposed a fast variational detection method for stripe noise based on interval sampling, aiming to improve the detection efficiency of multi-column strip noise during the pushbroom imaging of mainstream satellites. Based on the variational modeling of stripe noise components and the optimal solution, this method can quickly determine stripe noise components through interval sampling and establishing an estimation model of stripe noise components with interval sampling parameters. Then, this method can locate the stripe noise through the one-dimensional outlier detection and post-processing of the column mean values of stripe noise components. Owing to the interval sampling strategy, the method proposed in this study significantly improves the detection efficiency without impairing the stripe noise detection accuracy.

Keywords strip noise variational model automatic detection

ZTFLH:

TP79

Issue Date: 19 September 2023

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	Yuchuan BAI
	Rui XU
	Zongrui LI
	Jun PAN

Cite this article:

Yuchuan BAI,Rui XU,Zongrui LI, et al. Fast variational detection of stripe noise based on interval sampling[J]. Remote Sensing for Natural Resources, 2023, 35(3): 71-79.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022161 OR https://www.gtzyyg.com/EN/Y2023/V35/I3/71

Fig.1 Strip noise by strip pattern

Fig.2 Flowchart of the fast variation strip noise detection method based on interval sampling

Fig.3 Statistical-based debanding results for Terra MODIS band 33^[19]

Fig.4 Histogram of the mean of the strip noise component columns for remote sensing images without strip noise

Fig.5 Strip noise remote sensing images

Tab.1 Experimental results of stripe noise detection with different downsampling methods

Fig.6 Strip noise remote sensing images

Tab.2 Experimental results of stripe noise detection with different downsampling multipliers

Fig.7 Remote sensing images without strip noise

检测方法	$T N$	$F P$	$e r r$
基于间隔采样的快速变分条带噪声检测方法	5 700	0	0
自适应条带噪声检测方法	4 796	904	0.159
基于滑动窗口的多列条带噪声检测方法	4 042	1 658	0.291

Tab.3 Strip noise detection results of remote sensing images without strip noise

Fig.8 Strip noise remote sensing images

检测方法	$T P$	$T N$	$F P$	$F N$	$p$	$r$	$F 1$
基于间隔采样的快速变分条带噪声检测方法	6	5 693	0	1	1.000	0.857	0.923
自适应条带噪声检测方法	3	5 692	1	4	0.750	0.429	0.545
基于滑动窗口的多列条带噪声检测方法	4	5 653	40	3	0.091	0.571	0.157

Tab.4 Stripe noise detection results of stripe noise remote sensing image

[1]	蒋耿明, 牛铮, 阮伟利, 等. MODIS影像条带噪声去除方法研究[J]. 遥感技术与应用, 2003(6):393-398.
[1]	Jiang G M, Niu Z, Ruan W L, et al. A study on removing the stripe noises in MODIS 1B images[J]. Remote Sensing Technology and Application, 2003(6):393-398.
[2]	曹艳丽. 高光谱图像条带噪声滤除技术的研究[D]. 哈尔滨: 哈尔滨工程大学, 2011.
[2]	Cao Y L. The research on filtering technology of stripe noise on hyperspectral image[D]. Harbin: Harbin Engineering University, 2011.
[3]	Zhang J, Xing L Y, Cui H L, et al. A new algorithm for stripe noises detection and removal in image processing[C]// Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 891314, 2013:1-6.
[4]	Tsai F, Chen W W. Striping noise detection and correction of remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(12):4122-4131. doi: 10.1109/TGRS.2008.2000646 url: http://ieeexplore.ieee.org/document/4683341/
[5]	Liu Q H, Feng J. A destriping method combining strong filter with weak filter based on image divided and adapative strip noise detection[J]. Physics Procedia, 2012, 25:2103-2108. doi: 10.1016/j.phpro.2012.03.356 url: https://linkinghub.elsevier.com/retrieve/pii/S1875389212007729
[6]	胡旭东. 高分一号卫星遥感影像条带噪声检测与修复[D]. 武汉: 武汉大学, 2017.
[6]	Hu X D. Strip noise detecting and removal of GF-1 satellite images[D]. Wuhan: Wuhan University, 2017.
[7]	Horn B K P, Woodham R J. Destriping Landsat MSS images by histogram modification[J]. Computer Graphics and Image Processing, 1979, 10(1):69-83. doi: 10.1016/0146-664X(79)90035-2 url: https://linkinghub.elsevier.com/retrieve/pii/0146664X79900352
[8]	Cao B, Du Y, Xu D, et al. An improved histogram matching algorithm for the removal of striping noise in optical remote sensing imagery[J]. Optik - International Journal for Light and Electron Optics, 2015, 126(23):4723-4730. doi: 10.1016/j.ijleo.2015.08.079 url: https://linkinghub.elsevier.com/retrieve/pii/S0030402615008402
[9]	Gadallah F L, Csillag F, Smith E J M. Destriping multisensor imagery with moment matching[J]. International Journal of Remote Sensing, 2000, 21(12):2505-2511. doi: 10.1080/01431160050030592 url: https://www.tandfonline.com/doi/full/10.1080/01431160050030592
[10]	邢坤, 岳春宇, 刘爽, 等. 一种基于空间分割的矩匹配卫星影像条带噪声去除方法:中国,201310577168.4[P].2014-03-26.
[10]	Xing K, Yue C Y, Liu S, et al. A spatial segmentation based moment matching satellite image stripe noise removal method:China,201310577168.4[P].2014-03-26.
[11]	Chen J, Lin H, Shao Y, et al. Oblique striping removal in remote sensing imagery based on wavelet transform[J]. International Journal of Remote Sensing, 2006, 27(8):1717-1723. doi: 10.1080/01431160500185516 url: https://www.tandfonline.com/doi/full/10.1080/01431160500185516
[12]	Zhao B, He B, Cong Y. Destriping method using lifting wavelet transform of remote sensing image[C]// 2010 International Conference on Computer,Mechatronics,Control and Electronic Engineering, 2010, 6:110-113.
[13]	Pan J J, Chang C I. Destriping of Landsat MSS images by filtering techniques[J]. Photogrammetric Engineering and Remote Sensing, 1992, 58(10):1417-1423
[14]	Chen J, Shao Y, Guo H, et al. Destriping CMODIS data by power filtering[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(9):2119-2124. doi: 10.1109/TGRS.2003.817206 url: http://ieeexplore.ieee.org/document/1232225/
[15]	Münch B, Trtik P, Marone F, et al. Stripe and ring artifact removal with combined wavelet-Fourier filtering[J]. Optics Express, 2009, 17(10):8567-8591. doi: 10.1364/oe.17.008567 pmid: 19434191
[16]	Pande-Chhetri R, Abd-Elrahman A. De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(5):620-636. doi: 10.1016/j.isprsjprs.2011.04.003 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271611000530
[17]	Shen H F, Zhang L P. A map-based algorithm for destriping and inpainting of remotely sensed images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(5):1492-1502. doi: 10.1109/TGRS.2008.2005780 url: http://ieeexplore.ieee.org/document/4703209/
[18]	Liu X, Lu X, Shen H, et al. Stripe noise separation and removal in remote sensing images by consideration of the global sparsity and local variational properties[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5):1-12. doi: 10.1109/TGRS.2015.2471975 url: http://ieeexplore.ieee.org/document/7365529/
[19]	Chen L, Sun D, Toh K C. A note on the convergence of ADMM for linearly constrained convex optimization problems[J]. Computational Optimization and Applications, 2017, 66:327-343. doi: 10.1007/s10589-016-9864-7 url: http://link.springer.com/10.1007/s10589-016-9864-7
[20]	Sun Y J, Huang T Z, Ma T H, et al. Remote sensing image stripe detecting and destriping using the joint sparsity constraint with iterative support detection[J]. Remote Sensing, 2019, 11(6):608. doi: 10.3390/rs11060608 url: https://www.mdpi.com/2072-4292/11/6/608
[21]	王昶, 张永生, 王旭, 等. 遥感影像条带噪声去除的小波变分法[J]. 测绘学报, 2019, 48(8):1025-1037. doi: 10.11947/j.AGCS.2019.20180394
[21]	Wang C, Zhang Y S, Wang X, et al. Stripe noise removal of remote image based on wavelet variational method[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8):1025-1037. doi: 10.11947/j.AGCS.2019.20180394
[22]	赵书珩. 月球高光谱遥感影像复杂条带噪声处理的低秩方法研究[D]. 武汉: 武汉大学, 2021.
[22]	Zhao S H. Stripe noise removal for lunar hyperspectral imagery employing low-rank framework[D]. Wuhan: Wuhan University, 2021.
[23]	赵英时. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003: 87-91.
[23]	Zhao Y S. Principles and methods of remote sensing application analysis[M]. Beijing: Science Press, 2003:87-91.
[24]	薛利军, 李自田, 李长乐, 等. 光谱成像仪CCD焦平面组件非均匀性校正技术研究[J]. 光子学报, 2006(5):693-696.
[24]	Xue L J, Li Z T, Li C L, et al. Study on the hyper-spectral CCD imager non-uniformity correction algorithm[J]. Acta Photonica Sinica, 2006(5):693-696.
[25]	刘欣鑫. 光学遥感影像复杂条带噪声的变分处理方法研究[D]. 武汉: 武汉大学, 2018.
[25]	Liu X X. Stripe noise removal in remote sensing images by variational methods[D]. Wuhan: Wuhan University, 2018.
[26]	闫小明, 胡旭东, 尹烁. 卫星遥感影像信息缺失检测[J]. 测绘与空间地理信息, 2020, 43(7):153-155,159.
[26]	Yan X M, Hu X D, Yin S. Missing information detection on satellite remote sensing images[J]. Geomatics and Spatial Information Technology, 2020, 43(7):153-155,159.

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[2]	LI Chao, WANG Xin-yuan, LUO Lei, JI Wei. Automatic Detection of Lunar Elliptical Craters from Apollo Image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 71-75.

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