一种星载面阵相机序列影像的系统噪声去除方法

吴章平, 王密, 陈儒

吴章平, 王密, 陈儒. 一种星载面阵相机序列影像的系统噪声去除方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230361
引用本文: 吴章平, 王密, 陈儒. 一种星载面阵相机序列影像的系统噪声去除方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230361
WU Zhangping, WANG Mi, CHEN Ru. A System Noise Removal Method for Series Images from Spaceborne Area Array Camera[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230361
Citation: WU Zhangping, WANG Mi, CHEN Ru. A System Noise Removal Method for Series Images from Spaceborne Area Array Camera[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230361

一种星载面阵相机序列影像的系统噪声去除方法

基金项目: 

国家重点研发计划(2022YFB3902804)

详细信息
    作者简介:

    吴章平,硕士,研究方向为遥感影像相对辐射校正和质量提升。 wuzp2318@whu.edu.cn

    通讯作者:

    王密,教授,博士生导师。 wangmi@whu.edu.cn

  • 中图分类号: P237

A System Noise Removal Method for Series Images from Spaceborne Area Array Camera

  • 摘要: 由于面阵相机在轨辐射定标困难,当相对辐射定标系数不够准确或者卫星状态发生变化时,面阵成像遥感影像中会出现大量系统噪声。本文分析了系统噪声的来源,并基于序列影像系统噪声在时间轴上存在相关性的特点,提出了一种利用序列影像去除系统噪声的方法。本方法首先将原始影像与高斯滤波后的影像进行比值运算以去除辐射特征,得到纹理图像;然后将圆形局部二值模式(local binarypattern,LBP)算子和格拉布斯(Grubbs)准则结合滤除梯度特征,获得噪声图像;利用序列噪声图像叠加得到校正系数,实现系统噪声的去除。以模拟和真实获取的噪声图像作为实验数据,采用多种去噪方法进行对比实验,结果表明本文方法能够在去除噪声的同时,有效保留影像的边缘细节和纹理信息。
    Abstract: Objectives: Due to the difficulty of the on-orbit radiometric calibration of area-array camera, a large amount of system noise will appear in the area-array imaging remote sensing images when the relative radiometric calibration coefficients are not accurate enough or the satellite state changes. Most of the existing denoising methods are based on the spatial domain correlation or transform domain correlation of images, and the denoising images will have the problem of losing edge details and texture characteristics. We analyzes the source of the system noise and proposes a method to remove system noise using series images based on the correlation of system noise in the time axis of series images. Methods: First, the original image and the Gaussian filtered image are firstly combined in a ratio operation to eliminate the radiation characteristics to obtain multiple texture images. Second, the circular local binary pattern (LBP) operator and Grubbs criterion are combined to eliminate the gradient characteristics to obtain multiple noise images. Third, the correction coefficients are obtained by the superposition of multiple noise images, and further used to filter out the system noise. Results: The simulated and real images are used as the experimental data. Our proposed method was compared with non-local mean(NLM), discrete cosine transform(DCT), wavelet thresholding denoising, weighted nuclear norm minimization(WNNM), and block matching 3-D filtering(BM3D). Our method obtains the highest peak signal-to-noise ratio(PSNR) and structural similarity index(SSIM) with different noise levels in simulation experiments. For real data experiment, the laboratory coefficient correction, DCT and wavelet thresholding can not remove the noise well. BM3D and WNNM can efficiently remove noise and obtain high signal-to-noise ratio(SNR), but the edge and texture details are lost seriously. Our method removes the noise very well, and it can maintain the edge details and texture information. Conclusions: The results show that the method in this paper can effectively maintain image edge details and texture information while removing image noise.
  • [1] ZHAO Hongchen, ZHOU Xinghua, PENG Cong, et al. An Integrated BM3D Method for Removing Mixed Noise in Remote Sensing Image[J]. Geomatics and Information Science of Wuhan University, 2019, 44(06):925-932.(赵洪臣,周兴华,彭聪等.一种去除遥感影像混合噪声的集成BM3D方法[J].武汉大学学报(信息科学版),2019,44(06):925-932.)
    [2] DUAN Yini, ZHANG Lifu, YAN Lei, et al. Relative Radiometric Correction Methods for Remote Sensing Images and Their Applicability Analysis[J]. Journal of Remote Sensing, 2014, 18(03):597-617.(段依妮,张立福,晏磊等.遥感影像相对辐射校正方法及适用性研究[J].遥感学报,2014,18(03):597-617.)
    [3]

    Pan H, Gao F, Dong J, et al. Multiscale Adaptive Fusion Network for Hyperspectral Image Denoising[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16:3045-3059.

    [4]

    Shao L, Yan R, Li X, et al. From Heuristic Optimization to Dictionary Learning:A Review and Comprehensive Comparison of Image Denoising Algorithms[J]. IEEE transactions on cybernetics, 2013, 44(7):1001-1013.

    [5]

    Buades A, Coll B, Morel J M. A Review of Image Denoising Algorithms, with a New One[J]. Multiscale modeling & simulation, 2005, 4(2):490- 530.

    [6]

    Dabov K, Foi A, Katkovnik V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on image processing, 2007, 16(8):2080-2095.

    [7]

    Lebrun M, Buades A, Morel J M. A nonlocal Bayesian image denoising algorithm[J]. SIAM Journal on Imaging Sciences, 2013, 6(3):1665- 1688.

    [8]

    Gu S, Xie Q, Meng D, et al. Weighted nuclear norm minimization and its applications to low level vision[J]. International journal of computer vision, 2017, 121:183-208.

    [9]

    Mallat S G. A Theory for Multiresolution Signal Decomposition:the Wavelet Representation[J]. IEEE transactions on pattern analysis and machine intelligence, 1989, 11(7):674-693.

    [10]

    Othman H, Qian S E. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(2):397-408.

    [11]

    Rasti B, Sveinsson J R, Ulfarsson M O, et al. Hyperspectral image denoising using first order spectral roughness penalty in wavelet domain[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 7(6):2458- 2467.

    [12]

    Bayer F M, Kozakevicius A J, Cintra R J. An Iterative Wavelet Threshold for Signal Denoising[J]. Signal Processing, 2019, 162:10-20.

    [13]

    Liu L, Huan H, Li W, et al. Highly Sensitive Broadband Differential Infrared Photoacoustic Spectroscopy with Wavelet Denoising Algorithm for Trace Gas Detection[J]. Photoacoustics, 2021, 21:10.

    [14]

    Zhang Q, Yuan Q, Song M, et al. Cooperated spectral low-rankness prior and deep spatial prior for HSI unsupervised denoising[J]. IEEE Transactions on Image Processing, 2022, 31:6356-6368.

    [15]

    Ou Y, Luo J, Li B, et al. Gray-level image denoising with an improved weighted sparse coding[J]. Journal of Visual Communication and Image Representation, 2020, 72:102895.

    [16]

    Liu H, Li L, Lu J, et al. Group sparsity mixture model and its application on image denoising[J]. IEEE Transactions on Image Processing, 2022, 31:5677-5690.

    [17]

    Ou Y, Swamy M N S, Luo J, et al. Single image denoising via multi-scale weighted group sparse coding[J]. Signal Processing, 2022, 200:108650.

    [18]

    He W, Zhang H, Zhang L, et al. Total-variationregularized low-rank matrix factorization for hyperspectral image restoration[J]. IEEE transactions on geoscience and remote sensing, 2015, 54(1):178-188.

    [19]

    Xie Y, Qu Y, Tao D, et al. Hyperspectral image restoration via iteratively regularized schatten p-norm minimization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8):4642-4659.

    [20]

    Chen Y, Cao W, Pang L, et al. Hyperspectral image denoising with weighted nonlocal low-rank model and adaptive total variation regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-15.

    [21]

    Lin J, Huang T Z, Zhao X L, et al. A tensor subspace representation-based method for hyperspectral image denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(9):7739- 7757.

    [22]

    Chen Y, He W, Yokoya N, et al. Hyperspectral image restoration using weighted group sparsity-regularized low-rank tensor decomposition[J]. IEEE transactions on cybernetics, 2019, 50(8):3556- 3570.

    [23]

    Sun L, Cao Q, Chen Y, et al. Mixed Noise Removal for Hyperspectral Images Based on Global Tensor Low-Rankness and Nonlocal SVD-Aided Group Sparsity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61:1-17.

    [24]

    Huang Z, Zhang Y, Li Q, et al. Joint analysis and weighted synthesis sparsity priors for simultaneous denoising and destriping optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(10):6958-6982.

    [25]

    Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian Denoiser:Residual Learning of Deep CNN for Image Denoising[J]. IEEE transactions on image processing, 2017, 26(7):3142-3155.

    [26]

    Zhang K, Zuo W, Zhang L. FFDNet:Toward a Fast and Flexible Solution for CNN-based Image Denoising[J]. IEEE Transactions on Image Processing, 2018, 27(9):4608-4622.

    [27]

    Guo S, Yan Z, Zhang K, et al. Toward Convolutional Blind Denoising of Real Photographs[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019:1712-1722.

    [28]

    Liu M, Jiang W, Liu W, et al. Dynamic Adaptive Attention Guided Self-Supervised Single Remote Sensing Image Denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023.

    [29]

    Feng X, Zhang W, Su X, et al. Optical remote sensing image denoising and super-resolution reconstructing using optimized generative network in wavelet transform domain[J]. Remote Sensing, 2021, 13(9):1858.

    [30]

    Huang Z, Zhang Y, Li Q, et al. Unidirectional variation and deep CNN denoiser priors for simultaneously destriping and denoising optical remote sensing images[J]. International Journal of Remote Sensing, 2019, 40(15):5737-5748.

    [31]

    Nguyen H V, Ulfarsson M O, Sveinsson J R. Hyperspectral image denoising using SURE-based unsupervised convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(4):3369-3382.

    [32]

    Liu J, Sun Y, Xu X, et al. Image restoration using total variation regularized deep image prior[C]//ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Ieee, 2019:7715-7719.

    [33]

    Quan Y, Chen M, Pang T, et al. Self2self with dropout:Learning self-supervised denoising from single image[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020:1890-1898.

    [34] XU Heyu, ZHANG Liming, LI Xin, et al. A Relative Radiometric Calibration Method Based on Solar Diffuser Research for a Linear Array CCD Detector[J]. Acta Optica Sinica, 2020, 40(06):179- 187.(许和鱼,张黎明,李鑫等.基于太阳漫反射板线阵CCD相对辐射定标方法研究[J].光学学报,2020,40(06):179-187.)
    [35]

    Chang X, He L. System Noise Removal for Gaofen-4 Area-array Camera[J]. Remote Sensing, 2018, 10(5):759.

    [36]

    Yu G, Sapiro G. DCT Image Denoising:A Simple and Effective Image Denoising Algorithm[J]. Image Processing On Line, 2011, 1:292-296.

    [37]

    Xiang R, Wang L, He Q. Image Denoising Algorithm Based on Wavelet Transform and Partial Differential Equations[J]. Commun. Technol, 2017, 50:30-37.

    [38]

    Cheng G, Han J, Lu X. Remote Sensing Image Scene Classification:Benchmark and State of the Art[J]. Proceedings of the IEEE, 2017, 105(10):1865-1883.

    [39]

    Chaib S, Liu H, Gu Y, et al. Deep feature fusion for VHR remote sensing scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(8):4775-4784.

    [40]

    Ji H, Yang H, Gao Z, et al. Few-shot scene classification using auxiliary objectives and transductive inference[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5.

    [41]

    Xia G, Yang W, Delon J, et al. Structural high-resolution satellite image indexing[J]. 2009.

    [42]

    Zheng R, Jiang X, Ma Y, et al. A Comparison of Quality Assessment Metrics on Image Resolution Enhancement Artifacts[C]//2022 International Conference on Culture-Oriented Science and Technology (CoST). IEEE, 2022:200-204.

    [43]

    Gao B C. An Operational Method for Estimating Signal to Noise Ratios From Data Acquired with Imaging Spectrometers[J]. Remote Sensing of Environment, 1993, 43(1):23-33.

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出版历程
  • 收稿日期:  2023-12-25
  • 网络出版日期:  2024-01-24

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