Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images

Lifu ZHANG; Xuhui LU; Yi CEN; Xuejian SUN

doi:10.11834/jrs.20219043

Technologies and Methodologies | Views : 0 下载量: 280 CSCD: 1 更多指标

PDF
Export
Share
Collection
Album

Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images
Vol. 25, Issue 7, Pages: 1411-1421(2021)
Published： 07 July 2021 ，
DOI： 10.11834/jrs.20219043

扫描看全文

张立福，鹿旭晖，岑奕，孙雪剑.2021.高光谱图像空间光谱维去相关噪声评估.遥感学报，25（7）： 1411-1421

Zhang L F，Lu X H，Cen Y and Sun X J. 2021. Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images. National Remote Sensing Bulletin， 25（7）：1411-1421
张立福，鹿旭晖，岑奕，孙雪剑.2021.高光谱图像空间光谱维去相关噪声评估.遥感学报，25（7）： 1411-1421 DOI： 10.11834/jrs.20219043.

Zhang L F，Lu X H，Cen Y and Sun X J. 2021. Optimized spatial and spectral decorrelation method for noise estimation in hyperspectral images. National Remote Sensing Bulletin， 25（7）：1411-1421 DOI： 10.11834/jrs.20219043.

摘要

高光谱图像噪声评估既是评价图像质量的重要内容，也是衡量传感器性能的重要指标。一般噪声评估方法通过对图像规则分割或利用某种距离准则对图像进行连续性分割，计算图像子块的局部标准差或多元线性回归的残差来实现对图像噪声的估计。但这些方法获取的图像子块并不是完全均匀的，图像子块中仍然会存在地物边界，导致图像噪声评估的结果不准确。为了有效提取图像中的均匀子块，本文提出了一种优化的空间光谱维去相关（OSSDC）方法，基于光谱角距离和欧氏距离双重判定，从光谱曲线的形状和数值上寻找相似像元，获取图像中的均匀子块，然后利用多元线性回归计算残差实现对图像噪声的估算。利用模拟图像和实际航空飞行实验获取的高光谱图像对优化算法进行检验，同时与几种常用噪声评估方法进行对比分析，结果表明优化后的算法计算结果更准确，稳定性和适用性优于其他方法。

Abstract

Noise estimation of hyperspectral images (HSIs) is not only a crucial part of image quality evaluation but is also an important index of sensor performance. Spatial and spectral decorrelation method is a widely used approach for estimating noise in HSIs. This method is based on the high correlation of HSIs in space and spectrum

and a pixel can be predicted well using its spatial and spectral neighbors. Any prediction error can be considered noise. A series of noise estimation algorithms

such as Spatial and Spectral Decorrelation (SSDC)

Residual-scaled Local Standard Deviation (RLSD)

and Homogeneous Region Division and Spectral Decorrelation (HRDSDC)

have been developed on this basis.

The images are divided by rule or by some distances between spectrums in the general noise estimated methods. The local standard deviations or the residuals of multiple linear regression of imaging blocks are calculated as the image noise estimation. However

the sub-blocks of the images acquired by these methods are not completely uniform

and the edges of objects are still retained

thereby resulting in inaccurate outcomes of the image noise estimation. To obtain the uniform imaging blocks in the image effectively

an optimized SSDC method for estimating noise in HSIs has been used. The spectral angle and Euclidean distance are used to obtain the uniform imaging blocks

and the residuals of the heterogeneous blocks are calculated by multiple linear regression as the estimation of image noise. The optimized method is validated with simulated and radiance images acquired in the same aerial experiment and is compared with several useful noise estimation methods (e.g.

LMLSD

RLSD

SSDC

and HRDSDC). The LMLSD method

which is based on spatial dimension

is susceptible to image texture features and is only suitable for images with relatively uniform landcover. The RLSD method has better noise estimation results than LMLSD.

However

the uncertainty of the results is large and cannot indicate the noise level of images accurately. The three methods

namely

SSDC

HRDSDC and OSSDC

are all based on the spatial and spectral dimensions

have high stability

and can be applied to various images. The results of HRDSDC are significantly better than those of SSDC

and the OSSDC method exhibits better performance than HRDSDC. The OSSDC method uses the spectral angle and the Euclidean distance to determine the heterogeneous blocks

which reduce the influence of the edge of objects and the texture features. The results of image noise estimation are also accurate. In the validation

the optimized method shows distinctly enhanced robustness compared with the common methods. The estimation of the noise is also proved to be accurate. In addition

the effect of texture features on noise estimation is discussed in this paper. Results show that larger noise estimation results yield complex texture features.

关键词

高光谱影像噪声评估空间光谱维去相关法图像质量评估传感器性能评价

Keywords

hyperspectral imagenoise estimationspatial and spectralde-correlationimage quality assessmentsensor performance evaluation

references

Chen Q L and Xue Y Q. 2000. Estimation of signal-noise-ratio from data acquired with OMIS. Journal of Remote Sensing, 4(4): 284-289

陈秋林, 薛永祺. 2000. OMIS成像光谱数据信噪比的估算. 遥感学报, 4(4): 284-289 [DOI: 10.11834/jrs.20000408http://dx.doi.org/10.11834/jrs.20000408]

Corner B R, Narayanan R M and Reichenbach S E. 2003. Noise estimation in remote sensing imagery using data masking. International Journal of Remote Sensing, 24(4): 689-702 [DOI: 10.1080/01431160210164271http://dx.doi.org/10.1080/01431160210164271]

Curran P J and Dungan J L. 1989. Estimation of signal-to-noise: a new procedure applied to AVIRIS data. IEEE Transactions on Geoscience and Remote Sensing, 27(5): 620-628 [DOI: 10.1109/TGRS.1989.35945http://dx.doi.org/10.1109/TGRS.1989.35945]

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]

Fu P, Sun X and Sun Q S. 2018. Estimation of signal-dependent and -independent noise from hyperspectral images using a wavelet-based superpixel model. Remote Sensing Letters, 9(9): 906-915 [DOI: 10.1080/2150704X.2018.1492171http://dx.doi.org/10.1080/2150704X.2018.1492171]

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 deviations. 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]

Goetz A F H, Vane G, Solomon J E and Rock B N. 1985. Imaging spectrometry for earth remote sensing. Science, 228(4704): 1147-1153 [DOI: 10.1126/science.228.4704.1147http://dx.doi.org/10.1126/science.228.4704.1147]

Jiang Q S and Wang J Y. 2003. Study on signal-to-noise ratio estimation and compression method of operational modular imaging spectrometer multi-spectral images. Acta Optica Sinica, 23(11): 1335-1340

蒋青松, 王建宇. 2003. 实用型模块化成像光谱仪多光谱图像的信噪比估算及压缩方法研究. 光学学报, 23(11): 1335-1340 [DOI: 10.3321/j.issn:0253-2239.2003.11.012http://dx.doi.org/10.3321/j.issn:0253-2239.2003.11.012]

Mahmood A, Robin A and Sears M. 2014. Estimation of correlated noise in hyperspectral images//Proceedings of the 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Lausanne: IEEE: 1-4 [DOI: 10.1109/WHISPERS.2014.8077550http://dx.doi.org/10.1109/WHISPERS.2014.8077550]

Mahmood A, Robin A and Sears M. 2017. Modified residual method for the estimation of noise in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 55(3): 1451-1460 [DOI: 10.1109/TGRS.2016.2624505http://dx.doi.org/10.1109/TGRS.2016.2624505]

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]

Stein D W J, Beaven S G, Hoff L E, Winter E M, Schaum A P and Stocker A D. 2002. Anomaly detection from hyperspectral imagery. IEEE Signal Processing Magazine, 19(1): 58-69 [DOI: 10.1109/79.974730http://dx.doi.org/10.1109/79.974730]

Sun Y L, Zhang X, Shuai T, Shang K and Feng S N. 2015. Radiometric normalization of hyperspectral satellite images with spectral angle distance and Euclidean distance. Journal of Remote Sensing, 19(4): 618-626

孙艳丽, 张霞, 帅通, 尚坤, 冯淑娜. 2015. 光谱角—欧氏距离的高光谱图像辐射归一化. 遥感学报, 19(4): 618-626 [DOI: 10.11834/jrs.20154176http://dx.doi.org/10.11834/jrs.20154176]

Tong Q X, Zhang B and Zhang L F. 2016. Current progress of hyperspectral remote sensing in China. Journal of Remote Sensing, 20(5): 689-707

童庆禧, 张兵, 张立福. 2016. 中国高光谱遥感的前沿进展. 遥感学报, 20(5): 689-707 [DOI: 10.11834/jrs.20166264http://dx.doi.org/10.11834/jrs.20166264]

Wrigley R C, Card D H, Hlavka C A, Hall J R, Mertz F C, Archwamety C and Schowengerdt R A. 1984. Thematic Mapper image quality: Registration, noise, and resolution. IEEE Transactions on Geoscience and Remote Sensing, GE-22(3): 263-271 [DOI: 10.1109/TGRS.1984.350620http://dx.doi.org/10.1109/TGRS.1984.350620]

Zhang B. 2016. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20(5): 1062-1090

张兵. 2016. 高光谱图像处理与信息提取前沿. 遥感学报, 20(5): 1062-1090 [DOI: 10.11834/jrs.20166179http://dx.doi.org/10.11834/jrs.20166179]

Zhu B, Wang X H, Tang L L and Li C R. 2010. Review on methods for SNR estimation of optical remote sensing imagery. Remote Sensing Technology and Application, 25(2): 303-309

朱博, 王新鸿, 唐伶俐, 李传荣. 2010. 光学遥感图像信噪比评估方法研究进展. 遥感技术与应用, 25(2): 303-309

Alert me when the article has been cited

提交

Multi-scale feature extraction of hyperspectral image with guided filtering

Research progress on unmixing of hyperspectral remote sensing imagery

Evaluating the consistency of multi-source wideband remote sensing images:A band simulation approach using Hyperion data