An Effective Strip Noise Removal Method for Remote Sensing Image

doi:10.11947/j.JGGS.2022.0407

Abstract

Abstract:

In this paper, an efficient technique for removing strip noise from remote sensing images is proposed in order to better retain image details. Firstly, the remote sensing image with strip noise is decomposed by wavelet technology; Secondly, two variational models are constructed, stripe preserve variation model and a destriping variation model. In order to efficiently separate the detail information in the low level high-frequency component, the stripe preserve variation model eliminates the detail information from the low level high-frequency component (including strip noise) while maintaining the strip noise (including strip noise). In order to successfully save the details in the high level high-frequency component, the destriping variation model eliminates the strip noise in the high level high-frequency component (including the strip noise). Finally, wavelet reconstruction is used to get the denoised image. It is clear from a comparison with previous approaches that the suggested method not only successfully removes strip noise but also preserves image details.

Key words: wavelet transform; stripe preserve variation model; destriping variation model; high-frequency component; remote image

Chang WANG, Yongsheng ZHANG, Xu WANG, Song JI. An Effective Strip Noise Removal Method for Remote Sensing Image[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(4): 72-85.

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References 25

[1]	SUN Bin, LI Jinglin, ZHANG Xingxiang, et al. Interleaving assembly of TDICCDs on 600mm focal plane[J]. Optics and Precision Engineering, 2014, 22(11):2908-2913. doi: 10.3788/OPE.
[2]	WANG Renxiang, WANG Jianrong, HU Xin, et al. First practice of LMCCD camera imagery photogrammetry[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(3):221-225. DOI:10.13485/j.cnki.11-2089.2014.0032. doi: 10.13485/j.cnki.11-2089.2014.0032
[3]	CHEN Jinsong, SHAO Yun, GUO Huadong, 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
[4]	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.
[5]	TORRES J, INFANTE S O. Wavelet analysis for the elimination of striping noise in satellite images[J]. Optical Engineering, 2001, 40(7):1309-1314. doi: 10.1117/1.1383996
[6]	CHEN Jinsong, LIN Hui, SHAO Yun, 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
[7]	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
[8]	MVNCH 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
[9]	RAKWATIN P, TAKEUCHI W, YASUOKA Y. Stripe noise reduction in MODIS data by combining histogram matching with facet filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6):1844-1856. doi: 10.1109/TGRS.2007.895841
[10]	CARFANTAN H, IDIER J. Statistical linear destriping of satellite-based pushbroom-type images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(4):1860-1871. doi: 10.1109/TGRS.2009.2033587
[11]	SUN Lixin, NEVILLE R, STAENZ K, et al. Automatic destriping of Hyperion imagery based on spectral moment matching[J]. Canadian Journal of Remote Sensing, 2008, 34(S1):S68-S81. doi: 10.5589/m07-067
[12]	HU Baopeng, ZHOU Zeming, MENG Yong, et al. Destriping model of MODIS images based on moment matching and variational approach[J]. Infrared, 2014, 35(11):28-36.
[13]	HAN Ling, DONG Lianfeng, ZHANG Min, et al. Destriping hyperspectral image based on an improved moment matching method[J]. Acta Optica Sinica, 2009, 29(12):3333-3338. doi: 10.3788/AOS
[14]	SHEN Huanfeng, ZHANG Liangpei. 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
[15]	BOUALI M, LADJAL S. Toward optimal destriping of MODIS data using a unidirectional variational model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8):2924-2935. doi: 10.1109/TGRS.2011.2119399
[16]	CHANG Yi, FANG Houzhang, YAN Luxin, et al. Robust destriping method with unidirectional total variation and framelet regularization[J]. Optics Express, 2013, 21(20):23307-23323. doi: 10.1364/OE.21.023307 pmid: 24104244
[17]	ZHANG Yaozong, ZHOU Gang, YAN Luxin, et al. A destriping algorithm based on TV-Stokes and unidirectional total variation model[J]. Optik, 2016, 127(1):428-439. doi: 10.1016/j.ijleo.2015.09.246
[18]	HUO Lijun, HE Bin, ZHOU Dabiao. A destriping method with multi-scale variational model for remote sensing images[J]. Optics and Precision Engineering, 2017, 25(1):198-207. doi: 10.3788/OPE.
[19]	JI Tengyun, HUANG Tingzhu, ZHAO Xile, et al. Tensor completion using total variation and low-rank matrix factorization[J]. Information Sciences, 2016(326):243-257.
[20]	LIU Xinxin, LU Xiliang, SHEN Huanfeng, 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):3049-3060. doi: 10.1109/TGRS.2015.2510418
[21]	LIU Xinxin, SHEN Huanfeng, YUAN Qiangiang, et al. A universal destriping framework combining 1-D and 2-D variational optimization methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2):808-822. doi: 10.1109/TGRS.2017.2755016
[22]	SONG Qiong, WANG Yuehuan, YAN Xiaoyun, et al. Remote sensing images stripe noise removal by double sparse regulation and region separation[J]. Remote Sensing, 2018, 10(7):998. doi: 10.3390/rs10070998
[23]	BOUTEMEDJET A, DENG Chenwei, ZHAO Baojun. Edge-aware unidirectional total variation model for stripe non-uniformity correction[J]. Sensors, 2018, 18(4):1164. doi: 10.3390/s18041164
[24]	CHEN Yong, HUANG Tingzhu, ZHAO Xile, et al. Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint[J]. Remote Sensing, 2017, 9(6):559. doi: 10.3390/rs9060559
[25]	DOU Hongxia, HUANG Tingzhu, DENG Liangjian, et al. Stripe noise removal of remote sensing image with a directional l₀ sparse model[C]// Proceedings of 2017 IEEE International Conference on Image Processing.Beijing:IEEE, 2017:3505-3509.

Method	Literature[8]	Literature[12]	Literature[17]	Literature[18]	The proposed method
IF/BD	22.1005	26.9078	27.8844	28.0945	28.1159
ID	0.9513	0.9804	0.9875	0.9917	0.9933
IC	43.6399	44.1238	44.3586	44.4571	44.6030
CE/s	1.02	5.96	1.45	2.22	1.92

Method	Literature[8]	Literature[12]	Literature[17]	Literature[18]	The proposed method
IF/BD (MODIS30)	9.6654	12.8573	14.2842	14.4603	14.5488
ID	0.9545	0.9812	0.9862	0.9886	0.9901
IC	39.0835	41.0848	42.1848	42.4567	42.8974
CE/s	1.45	5.56	2.25	3.33	2.78
IF/BD (Hyperion)	10.3512	13.4528	14.6369	14.9143	15.0705
ID	0.9604	0.9784	0.9835	0.9869	0.9892
IC	33.9874	34.7263	35.4255	35.6239	35.9647
CE/s	0.22	4.62	0.86	1.48	1.04
IF/BD (CHRIS)	22.9732	25.0878	26.1845	26.8612	26.9104
ID	0.9625	0.9811	0.9865	0.9900	0.9914
IC	49.8916	52.1676	53.3137	53.5939	54.0552
CE/s	2.66	7.32	3.32	4.26	3.75
IF/BD (Aerial image 1)	8.9664	11.3103	11.4886	11.5981	11.6287
ID	0.9514	0.9824	0.9865	0.9877	0.9884
IC	42.9153	43.6418	44.6422	45.3483	45.7032
CE/s	0.56	5.21	1.47	2.05	1.68
IF/BD(Aerial image 2)	10.6849	13.8732	14.5231	14.7631	15.0045
ID	0.9538	0.9831	0.9872	0.9886	0.9908
IC	42.0830	42.6678	43.2578	43.5185	43.8216
CE/s	0.28	4.75	0.95	1.57	1.12