浏览全部资源
扫码关注微信
Published： 07 May 2021 ，
扫 描 看 全 文
章硕，孙斌，李树涛，康旭东.2021.空间与光谱维度的高光谱图像噪声估计.遥感学报，25（5）： 1108-1123
Zhang S，Sun B，Li S T and Kang X D. 2021. Noise estimation of hyperspectral image in the spatial and spectral dimensions. National Remote Sensing Bulletin， 25（5）：1108-1123
高光谱图像能够获取地物精细的光谱诊断特征，但受限于多谱段分光的成像机制，图像各个谱段上光成像的能量不足，信噪比难以提升。高光谱图像噪声类型与强度的准确估计，是提升高光谱图像去噪性能的关键，也是优化其成像系统设计的重要依据。现有高光谱图像噪声估计算法通常将不同类型的图像噪声作为一个整体，并未充分考虑不同类型噪声的区别。本文从高光谱图像获取的机理出发，提出了一种联合空间与光谱维度分析的高光谱图像噪声估计方法。首先，建立了高光谱图像噪声退化模型，将图像中的主要噪声定义为两类：条带与高斯噪声。然后，基于条带噪声在空间维度上独特的频率特性，提出了基于傅里叶变换与局部均值滤波的条带噪声估计方法。最后，基于在光谱维度上高光谱图像相邻波段间的高相关性，通过多元回归分析估计高斯噪声的均值与标准差。本文在模拟高光谱噪声数据上进行算法验证的同时，深入分析了高分五号短波红外高光谱相机、机载Nano-Hyperspec成像仪等国内外成像仪获取的真实高光谱数据。实验结果表明，本文提出的噪声估计方法能够有效的估计出高光谱图像不同谱段条带与高斯噪声的量化指标。实验结果可用于分析高光谱图像在不同传感器与不同成像场景下的退化原因，从而设计更优的图像去噪方法与成像系统。
Given the influence of imaging environment and equipment limits
hyperspectral images (HSIs) are often disturbed by noise. Thus
denoising is necessary for the subsequent image processing. Noise type and level are important parameters of the denoising algorithm. Furthermore
noise estimation can help people understand the image quality objectively. Many HSI noise estimation algorithms consider images to contain additive noise and measure the level of noise by estimating the statistical characteristics
such as standard deviation and co-variance. To our knowledge
the existing HSI noise estimation methods do not consider the specific type of noise. Unlike previous works
we propose a method that combines spatial and spectral domain analyses for the separation and estimation of different types of noise.
Considering the characteristics of HSI noise
the noise contained in a HSI is modeled in this work as the linear combination of stripe and Gaussian noise. According to the spatial characteristics (horizontal and vertical distribution) of stripe noise
after Fourier transform
stripe noise can be represented as a specific central cross line distribution in the Fourier spectrum map. Therefore
stripe noise can be separated and quantitatively estimated by processing the pixels on the cross line. However
the useful information in the HSI may also distribute on the central cross line in the Fourier spectrum map. To eliminate the estimation error of stripe noise
the heterogeneity function is introduced to decrease the estimation error
and local mean filtering is used to further separate stripe noise and useful signal. The level of stripe noise is estimated by the sum of the pixel values on the cross line. After removing the stripe noise
a method based on multiple regression is used to extract the Gaussian noise in an image. Using the correlation among adjacent bands and the randomness of noise
a single band image can be represented by the linear combination of the remaining bands and the residual. Given that residuals can be approximately represented as a Gaussian distribution
the mean and standard deviation of the extracted residuals are calculated to describe the distribution characteristics of Gaussian noise in different bands.
In the simulation experiment
image bands with stripe noise were detected successfully; the estimation Gaussian noise standard deviation is 0.0527 (theoretical value is 0.05)
the mean value is near the theoretical value of 0. Furthermore
seven HSIs captured by satellites GF-5 and airborne hyperspectral imager Nano-Hyperspec were tested. The estimation results of real-world HSIs show that the mean of Gaussian noise is very near 0 for each band
which is consistent with the assumption in most denoising algorithms. For the stripe noise
some distribution rules of stripe noise are provided.
In this study
we proposed a noise estimation method based on Fourier transform and multiple linear regression. This method can separate and estimate the level of the two types of noise. Experimental results show that the proposed method is efficient
and the noise levels of a HSI vary in different bands
sensors
and scenes. More importantly
the noise properties of HSIs were analyzed in this work. Some conclusions about the characteristic of HSI noise can be obtained.
高光谱图像噪声估计噪声分离傅里叶变换条带噪声高斯噪声
hyperspectral imagenoise estimationnoise separationfourier transformstripe noiseGaussian noise
Acito N, Diani M and Corsini G. 2011a. Subspace-based striping noise reduction in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 49(4): 1325-1342 [DOI: 10.1109/TGRS.2010.2081370http://dx.doi.org/10.1109/TGRS.2010.2081370]
Acito N, Diani M and Corsini G. 2011b. Signal-dependent noise modeling and model parameter estimation in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 49(8): 2957-2971 [DOI: 10.1109/TGRS.2011.2110657http://dx.doi.org/10.1109/TGRS.2011.2110657]
Aiazzi B, Alparone L, Barducci A, Baronti S, Marcoionni P, Pippi I and Selva M. 2006. Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers. Annals of Geophysics, 49(1): 1-9 [DOI: 10.4401/ag-3141http://dx.doi.org/10.4401/ag-3141]
Bioucas-Dias J M and Nascimento J M P. 2008. Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46(8): 2435-2445 [DOI: 10.1109/TGRS.2008.918089http://dx.doi.org/10.1109/TGRS.2008.918089]
Bourennane S, Fossati C and Lin T. 2018. Noise removal based on tensor modelling for hyperspectral image classification. Remote Sensing, 10(9): 1330 [DOI: 10.3390/rs10091330http://dx.doi.org/10.3390/rs10091330]
Chen J S, Shao Y, Guo H D, Wang W M and Zhu B Q. 2003. Destriping CMODIS data by power filtering. IEEE Transactions on Geoscience and Remote Sensing, 41(9): 2119-2124 [DOI: 10.1109/TGRS.2003.817206http://dx.doi.org/10.1109/TGRS.2003.817206]
Fu P, Sun Q S and Ji Z X. 2015. Noise estimation from remote sensing images by fractal theory and adaptive image block division. Acta Geodaetica et Cartographica Sinica, 44(11): 1235-1245
傅鹏, 孙权森, 纪则轩. 2015. 结合分形理论和自适应图像块划分的遥感图像噪声估计. 测绘学报, 44(11): 1235-1245 [DOI: 10.11947/j.AGCS.2015.20140327http://dx.doi.org/10.11947/j.AGCS.2015.20140327]
Fu P, Sun X and Sun Q S. 2017. Hyperspectral image segmentation via frequency-based similarity for mixed noise estimation. Remote Sensing, 9(12): 1237 [DOI: 10.3390/rs9121237http://dx.doi.org/10.3390/rs9121237]
Fujimotor N, Takahashi Y, Moriyama T, Shimada M, Wakabayashi H, Nakatani Y and Obayashi S. 1989. Evaluation of SPOT HRV image data received in Japan//Proceedings of the 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium. Vancouver, Canada: IEEE: 463-466 [DOI: 10.1109/IGARSS.1989.578740http://dx.doi.org/10.1109/IGARSS.1989.578740]
Gao B C. 1993. An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers. Remote Sensing of Environment, 43(1): 23-33 [DOI: 10.1016/0034-4257(93)90061-2http://dx.doi.org/10.1016/0034-4257(93)90061-2]
Gao L R, Zhang B, Zhang X and Shen Q. 2007. Study on the method for estimating the noise in remote sensing images based on local standard deviation. Journal of Remote Sensing, 11(2): 201-208
高连如, 张兵, 张霞, 申茜. 2007. 基于局部标准差的遥感图像噪声评估方法研究. 遥感学报, 11(2): 201-208 [DOI: 10.11834/jrs.20070227http://dx.doi.org/10.11834/jrs.20070227]
Gao L R, Zhang B, Zhang X, Zhang W J and Tong Q X. 2008. A new operational method for estimating noise in hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 5(1): 83-87 [DOI: 10.1109/LGRS.2007.909927http://dx.doi.org/10.1109/LGRS.2007.909927]
Li X H, Shen H F, Zhang L P, Zhang H Y and Yuan Q Q. 2014. Dead pixel completion of aqua MODIS band 6 using a robust M-estimator multiregression. IEEE Geoscience and Remote Sensing Letters, 11(4): 768-772 [DOI: 10.1109/LGRS.2013.2278626http://dx.doi.org/10.1109/LGRS.2013.2278626]
Liu D C, Tong Q L, Li Z Z, Zhao Y J, Yang Y J, Wang M Z, Xie T, Ye F W, Qiu J T and Wang Z T. 2019. Research on oil-gas exploration using airborne hyerspectral remote sensing and its application effect. Acta Geologica Sinica, 93(1): 272-284
刘德长, 童勤龙, 李志忠, 赵英俊, 杨燕杰, 王茂芝, 谢涛, 叶发旺, 邱骏挺, 王子涛. 2019. 航空高光谱遥感油气探测技术研究及应用效果. 地质学报, 93(1): 272-284 [DOI: 10.19762/j.cnki:dizhixuebao.20019015http://dx.doi.org/10.19762/j.cnki:dizhixuebao.20019015]
Liu N, Li W, Tao R and Fowler J E. 2019. Wavelet-domain low-rank/group-sparse destriping for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 10310-10321 [DOI: 10.1109/TGRS.2019.2933555http://dx.doi.org/10.1109/TGRS.2019.2933555]
Liu X X, Shen H F, Yuan Q Q, Lu X L and Zhou C P. 2018. A universal destriping framework combining 1-D and 2-D variational optimization methods. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 808-822 [DOI: 10.1109/TGRS.2017.2755016http://dx.doi.org/10.1109/TGRS.2017.2755016]
Liu Y N, Sun D X, Cao K Q, Liu S F, Chai M Y, Liang J and Yuan J. 2020. Evaluation of GF-5 AHSI on-orbit instrument radiometric performance. Journal of Remote Sensing, 24(4): 352-359
刘银年, 孙德新, 曹开钦, 刘书锋, 柴孟阳, 梁建, 原娟. 2020. 高分五号可见短波红外高光谱相机在轨辐射性能评估. 遥感学报, 24(4): 352-359 [DOI: 10.11834/jrs.20209258http://dx.doi.org/10.11834/jrs.20209258]
Pande-Chhetri R and Abd-Elrahman A. 2011. De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5): 620-636 [DOI: 10.1016/j.isprsjprs.2011.04.003http://dx.doi.org/10.1016/j.isprsjprs.2011.04.003]
Roger R E and Arnold J F. 1996. Reliably estimating the noise in AVIRIS hyperspectral images. International Journal of Remote Sensing, 17(10): 1951-1962 [DOI: 10.1080/01431169608948750http://dx.doi.org/10.1080/01431169608948750]
Shen H F, Li X H, Cheng Q, Zeng C, Yang G, Li H F and Zhang L P. 2015. Missing information reconstruction of remote sensing data: a technical review. IEEE Geoscience and Remote Sensing Magazine, 3(3): 61-85 [DOI: 10.1109/MGRS.2015.2441912http://dx.doi.org/10.1109/MGRS.2015.2441912]
Shen H F, Li X H, Zhang L P, Tao D C and Zeng C. 2014. Compressed sensing-based inpainting of aqua moderate resolution imaging spectroradiometer band 6 using adaptive spectrum-weighted sparse Bayesian dictionary learning. IEEE Transactions on Geoscience and Remote Sensing, 52(2): 894-906 [DOI: 10.1109/TGRS.2013.2245509http://dx.doi.org/10.1109/TGRS.2013.2245509]
Sun X. 2018. Research on Remote Sensing Image Noise Evaluation Method and System Implementation. Nanjing: Nanjing University of Science and Technology: 1-63
孙鑫. 2018. 遥感图像噪声评价方法研究及系统实现. 南京: 南京理工大学: 1-63
Tang Z Q, Fu G Y, Zhao X L, Chen J and Zhang L. 2016. Low-rank representation for hyperspectral image noise level estimation. Journal of Image and Graphics, 21(7): 942-950
唐中奇, 付光远, 赵晓林, 陈进, 张利. 2016. 高光谱图像低秩表达与噪声水平估计. 中国图象图形学报, 21(7): 942-950 [DOI: 10.11834/jig.20160712http://dx.doi.org/10.11834/jig.20160712]
Wang J Y, Wang Y M and Li C L. 2010. Noise model of hyperspectral imaging system and influence on radiation sensitivity. Journal of Remote Sensing, 14(4): 607-620
王建宇, 王跃明, 李春来. 2010. 高光谱成像系统的噪声模型和对辐射灵敏度的影响. 遥感学报, 14(4): 607-620 [DOI: 10.11834/jrs.20100401http://dx.doi.org/10.11834/jrs.20100401]
Xiang Y J, Zhang J F, Yang G and Wang Q. 2017. A mixed-noise estimation-based anomaly detection method for hyperspectral image. Infrared Technology, 39(8): 734-739
向英杰, 张俭峰, 杨桄, 王琪. 2017. 基于混合噪声估计的高光谱图像异常检测方法. 红外技术, 39(8): 734-739
Zhao Z M, Gao L R, Chen D, Yue A Z, Chen J B, Liu D S, Yang J and Meng Y. 2019. Development of satellite remote sensing and image processing platform. Journal of Image and Graphics, 24(12): 2098-2110
赵忠明, 高连如, 陈东, 岳安志, 陈静波, 刘东升, 杨健, 孟瑜. 2019. 卫星遥感及图像处理平台发展. 中国图象图形学报, 24(12): 2098-2110 [DOI: 10.11834/jig.190450http://dx.doi.org/10.11834/jig.190450]
Zhong Y F, Li W Q, Wang X Y, Jin S Y and Zhang L P. 2020. Satellite-ground integrated destriping network: a new perspective for EO-1 Hyperion and Chinese hyperspectral satellite datasets. Remote Sensing of Environment, 237: 111416 [DOI: 10.1016/j.rse.2019.111416http://dx.doi.org/10.1016/j.rse.2019.111416]
相关文章
相关作者
相关机构
京公网安备11010802024621