# Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Analysis of Stripe Noise

#### 2.1. Conversion of the Destriping Problem

#### 2.2. Scale Characteristics of Stripes

## 3. Methodology

#### 3.1. Overall Description of DUMD

#### 3.2. The Framework of Column-by-Column Nonuniformity Correction (CCNUC)

#### 3.3. Unidirectional Multiscale Decomposition

#### 3.4. Deviation Estimation of Adjacent Columns

#### 3.5. Cumulative Error Compensation

## 4. Results

#### 4.1. Experimental Data

#### 4.2. Experimental Results

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Wang, M.; Chen, C.; Pan, J.; Zhu, Y.; Chang, X. A Relative Radiometric Calibration Method Based on the Histogram of Side-Slither Data for High-Resolution Optical Satellite Imagery. Remote Sens.
**2018**, 10, 381. [Google Scholar] [CrossRef] - Huo, L.-J.; He, B.; Zhou, D.-B. A destriping method with multi-scale variational model for remote sensing images. Opt. Precis. Eng.
**2017**, 25, 198–207. [Google Scholar] - Huo, L.; Zhou, D.; Wang, D.; Liu, R.; He, B. Staircase-scene-based nonuniformity correction in aerial point target detection systems. Appl. Opt.
**2016**, 55, 7149–7156. [Google Scholar] [CrossRef] [PubMed] - Chen, J.; Shao, Y.; Guo, H.; Wang, W.; Zhu, B. Destriping CMODIS data by power filtering. IEEE Trans. Geosci. Remote Sens.
**2003**, 41, 2119–2124. [Google Scholar] [CrossRef] - Shi, G.-M.; Wang, X.-T.; Zhang, L.; Liu, Z. Removal of random stripe noises in remote sensing image by directional filter. J. Infrared Millim. Waves.
**2008**, 27, 214–218. [Google Scholar] [CrossRef] - Infante, O.S. Wavelet analysis for the elimination of striping noise in satellite images. Opt. Eng.
**2001**, 40, 1309. [Google Scholar] [CrossRef] - Münch, B.; Trtik, P.; Marone, F.; Stampanoni, M. Stripe and ring artifact removal with combined wavelet—Fourier filtering. Opt. Express.
**2009**, 17, 8567. [Google Scholar] [CrossRef] [PubMed] - Simpson, J.J.; Stitt, J.R.; Leath, D.M. Improved Finite Impulse Response Filters for Enhanced Destriping of Geostationary Satellite Data. Remote Sens. Environ.
**1998**, 66, 235–249. [Google Scholar] [CrossRef] - Pande-Chhetri, R.; Abd-Elrahman, A. De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering. ISPRS J. Photogramm. Remote Sens.
**2011**, 66, 620–636. [Google Scholar] [CrossRef] - Horn, B.K.; Woodham, R.J. Destriping LANDSAT MSS images by histogram modification. Comput. Graph. Image Process.
**1979**, 10, 69–83. [Google Scholar] [CrossRef][Green Version] - Weinreb, M.; Xie, R.; Lienesch, J.; Crosby, D. Destriping GOES images by matching empirical distribution functions. Remote Sens. Environ.
**1989**, 29, 185–195. [Google Scholar] [CrossRef] - Gadallah, F.L.; Csillag, F.; Smith, E.J.M. Destriping multisensor imagery with moment matching. Int. J. Remote Sens.
**2000**, 21, 2505–2511. [Google Scholar] [CrossRef] - Chang, W.W.; Guo, L.; Fu, Z.Y.; Liu, K. A new destriping method of imaging spectrometer images. In Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 2–4 November 2007. [Google Scholar]
- Shen, H.; Zhang, L. A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images. IEEE Trans. Geosci. Remote Sens.
**2009**, 47, 1492–1502. [Google Scholar] [CrossRef] - Bouali, M.; Ladjal, S. Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model. IEEE Trans. Geosci. Remote Sens.
**2011**, 49, 2924–2935. [Google Scholar] [CrossRef] - Zhou, G.; Fang, H.; Yan, L.; Zhang, T.; Hu, J. Removal of stripe noise with spatially adaptive unidirectional total variation. Optik
**2014**, 125, 2756–2762. [Google Scholar] [CrossRef] - Liu, X.; Lu, X.; Shen, H.; Yuan, Q.; Jiao, Y.; Zhang, L. Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties. IEEE Trans. Geosci. Remote Sens.
**2016**, 54, 3049–3060. [Google Scholar] [CrossRef] - Liu, Y.-X.; Hao, Z.-H. Research on the nonuniformity correction of linear TDI CCD remote camera. Opt. Tech.
**2003**, 5633, 527–535. [Google Scholar] - Chen, C.; Pan, J.; Wang, M.; Zhu, Y. Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly. Sensors
**2018**, 18, 3402. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**Stripe noise with different scales in image and corresponding column mean vector (CMV). Where (

**a**) is small-scale stripe, (

**b**) is large-scale stripe, (

**c**) is the CMV of (

**a**), (

**d**) is the CMV of (

**b**).

**Figure 2.**Overall flow chart of DUMD (destriping method via unidirectional multiscale decomposition). CCNUC, column-by-column nonuniformity correction.

**Figure 3.**Destriping performance of Data 3 (Gaofen-1C, GF-1C). MM, moment matching; IMM, improved MM; SAUTV, spatially adaptive unidirectional total variation. Where (

**a**) is the no-stripe image, (

**b**) is the striped image, (

**c**) is the destriped image processed by MM, (

**d**) is the destriped image processed by IMM, (

**e**) is the destriped image processed by SAUTV, (

**f**) is the destriped image processed by DUMD.

**Figure 4.**Destriping performance of Data 4 (Gaofen1-D, GF-1D). Where (

**a**) is the no-stripe image, (

**b**) is the striped image, (

**c**) is the destriped image processed by MM, (

**d**) is the destriped image processed by IMM, (

**e**) is the destriped image processed by SAUTV, (

**f**) is the destriped image processed by DUMD.

**Figure 5.**Destriping performance of Data 6 (Ziyuan3-02, ZY3-02). Where (

**a**) is the no-stripe image, (

**b**) is the striped image, (

**c**) is the destriped image processed by MM, (

**d**) is the destriped image processed by IMM, (

**e**) is the destriped image processed by SAUTV, (

**f**) is the destriped image processed by DUMD.

**Figure 6.**CMVs of Data 3 (GF-1C). Where (

**a**) is the CMVs of original image and striped image, (

**b**) is the CMVs of original image and MM-processed image, (

**c**) is the CMVs of original image and IMM-processed image, (

**d**) is the CMVs of original image and SAUTV-processed image, (

**e**) is the CMVs of original image and DUMD-processed image.

**Figure 7.**CMVs of Data 4 (GF-1D). Where (

**a**) is the CMVs of original image and striped image, (

**b**) is the CMVs of original image and MM-processed image, (

**c**) is the CMVs of original image and IMM-processed image, (

**d**) is the CMVs of original image and SAUTV-processed image, (

**e**) is the CMVs of original image and DUMD-processed image.

**Figure 8.**CMVs of Data 6 (ZY3-02). Where (

**a**) is the CMVs of original image and striped image, (

**b**) is the CMVs of original image and MM-processed image, (

**c**) is the CMVs of original image and IMM-processed image, (

**d**) is the CMVs of original image and SAUTV-processed image, (

**e**) is the CMVs of original image and DUMD-processed image.

Order | Satellite ID | Features | Image Size | Bandwidths (nm) | Resolution of MS (m) | |||
---|---|---|---|---|---|---|---|---|

B1 | B2 | B3 | B4 | |||||

1 | Beijing-2 | City | 6254 × 6071 | 440~510 | 510~590 | 600~670 | 760~910 | 3.2 |

2 | GF-1B | City and Mountain | 4333 × 3948 | 450~520 | 520~590 | 630~690 | 770~890 | 8 |

3 | GF-1C | Farmland and Cloud | 4387 × 4442 | 450~520 | 520~590 | 630~690 | 770~890 | 8 |

4 | GF-1D | Gulf and Mountain | 4278 × 4131 | 450~520 | 520~590 | 630~690 | 770~890 | 8 |

5 | GF-2 | Mountain | 7058 × 6705 | 450~520 | 520~590 | 630~690 | 770~890 | 4 |

6 | ZY3-02 | Farmland and Lake | 8625 × 8877 | 450~520 | 520~590 | 630~690 | 770~890 | 5.8 |

Index | Method | Data 1 | Data 2 | Data 3 | Data 4 | Data 5 | Data 6 | AVERAGE |
---|---|---|---|---|---|---|---|---|

RMSE | MM | 61.33 | 148.77 | 213.00 | 156.21 | 7.25 | 17.23 | 100.63 |

IMM | 47.14 | 59.83 | 70.08 | 68.02 | 9.37 | 11.17 | 44.27 | |

SAUTV | 44.28 | 57.82 | 69.37 | 59.65 | 9.72 | 8.99 | 41.64 | |

DUMD | 51.46 | 104.22 | 156.85 | 110.21 | 4.57 | 12.86 | 73.36 | |

SNR | MM | 25.21 | 19.50 | 17.95 | 19.48 | 29.19 | 25.38 | 22.78 |

IMM | 27.60 | 27.36 | 27.31 | 26.26 | 27.77 | 28.23 | 27.42 | |

SAUTV | 28.23 | 27.82 | 27.50 | 27.60 | 27.42 | 30.18 | 28.12 | |

DUMD | 26.82 | 22.53 | 20.46 | 22.16 | 33.19 | 27.17 | 25.39 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

He, L.; Wang, M.; Chang, X.; Zhang, Z.; Feng, X. Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition. *Remote Sens.* **2019**, *11*, 2472.
https://doi.org/10.3390/rs11212472

**AMA Style**

He L, Wang M, Chang X, Zhang Z, Feng X. Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition. *Remote Sensing*. 2019; 11(21):2472.
https://doi.org/10.3390/rs11212472

**Chicago/Turabian Style**

He, Luxiao, Mi Wang, Xueli Chang, Zhiqi Zhang, and Xiaoxiao Feng. 2019. "Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition" *Remote Sensing* 11, no. 21: 2472.
https://doi.org/10.3390/rs11212472