Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (2) : 60-66     DOI: 10.6046/gtzyyg.2017.02.09
Contents |
Hyperspectral data subspace dimension algorithm based on noise whitening
CHEN Jie1, 2, DU Lei1, LI Jing1, HAN Yachao1, GAO Zihong1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Download: PDF(1203 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  The correlation between adjacent bands of hyperspectral image data is relatively strong. However, signal coexists with noise. The HySime (hyperspectral signal identification by minimum error) algorithm which is based on the principle of least squares is designed to calculate the estimated noise value and the estimated signal correlation matrix value. The algorithm is effective with accurate noise value but ineffective with estimated noise value obtained from spectral dimension reduction and decorrelation process. This paper proposes an improved HySime algorithm based on noise whitening process. Instead of removing noise pixel by pixel, the algorithm carries out the noise whitening process on the original data first, obtains the noise covariance matrix estimated value accurately, and uses the HySime algorithm to calculate the signal correlation matrix value so as to improve the precision of the resultant value. Simulation and experiment have reached some conclusions: Firstly, the improved HySime algorithm is more accurate and stable than the original HySime algorithm; Secondly, the improved HySime algorithm results have better consistency under different conditions compared with the classic NSP (noise subspace the projection) algorithm; Finally, the improved HySime algorithm improves the adaptability of non-white data noise with the noise whitening process.
Keywords fog      inversion      physical parameters      influencing factor     
Issue Date: 03 May 2017
E-mail this article
E-mail Alert
Articles by authors
MA Huiyun
ZHAO Guoqing
ZOU Zhengrong
ZHANG Weikang
Cite this article:   
MA Huiyun,ZHAO Guoqing,ZOU Zhengrong, et al. Hyperspectral data subspace dimension algorithm based on noise whitening[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 60-66.
URL:     OR
[1] Green A A,Berman M,Switzer P,et al.A transformation for ordering multispectral data in terms of image quality with implications for noise removal[J].IEEE Transactions on Geoscience and Remote Sensing,1988,26(1):65-74.
[2] Lee J B,Woodyatt A S,Berman M.Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(3):295-304.
[3] Chang C I,Du Q.Interference and noise-adjusted principal components analysis[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2387-2396.
[4] 张 兵,高连如.高光谱图像分类与目标探测[M].北京:科学出版社,2011.
Zhang B,Gao L R.Hyperspectral Image Classification and Target Detection[M].Beijing:Science Press,2011.
[5] Roger R E.Principal components transform with simple, automatic noise adjustment[J].International Journal of Remote Sensing,1996,17(14):2719-2727.
[6] Roger R E,Arnold J F.Reliably estimating the noise in AVIRIS hyperspectral images[J].International Journal of Remote Sensing,1996,17(10):1951-1962.
[7] 洪 波.高光谱遥感图像信噪比估算方法研究[D].北京:中国科学院大学,2013.
Hong B.Study on Methods for SNR Estimation of Hyperspectral Remote Sensing Images[D].Beijing:University of Chinese Academy of Sciences,2013.
[8] 高连如.高光谱遥感目标探测中的信息增强与特征提取研究[D].北京:中国科学院遥感应用研究所,2007.
Gao L R.Research on Information Enhancement and Feature Extraction in Hyperspectral Remote Sensing Object Detection[D].Beijing:Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,2007.
[9] Chang C I,Du Q.Estimation of number of spectrally distinct signal sources in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(3):608-619.
[10] Bioucas-Dias J M,Nascimento J M P.Hyperspectral subspace identification[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(8):2435-2445.
[11] Eldar Y C,Oppenheim A V.MMSE whitening and subspace whitening[J].IEEE Transactions on Information Theory,2003,49(7):1846-1851.
[12] Cawse K,Robin A,Sears M.The effect of noise whitening on methods for determining the intrinsic dimension of a hyperspectral image[C]//Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing.Lisbon,Portugal:IEEE Computer Society,2011.
[13] Nascimento J M P,Dias J M B.Vertex component analysis:A fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910.
[14] Chang C I,Du Q.Noise subspace projection approaches to determination of intrinsic dimensionality of hyperspectral imagery[C]//Proceedings of the SPIE 3871,Image and Signal Processing for Remote Sensing V.Florence,Italy:SPIE,1999:34-44.
[15] Swayze G,Clark R N,Kruse F,et al.Ground-truthing AVIRIS mineral mapping at Cuprite,Nevada[C]//Summaries of the Third Annual JPL Airborne Geoscience Workshop.Denver,CO:JPL Publication,1992:47-49.
[16] Swayze G A.The Hydrothermal and Structural History of the Cuprite Mining District,Southwestern Nevada:An Integrated Geological and Geophysical Approach[D].Boulder,Colorado:University of Colorado,1997.
[17] Chang C I,Xiong W,Liu W M,et al.Linear spectral mixture analysis based approaches to estimation of virtual dimensionality in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(11):3960-3979.
[1] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[2] SHA Yonglian, WANG Xiaowen, LIU Guoxiang, ZHANG Rui, ZHANG Bo. SBAS-InSAR-based monitoring and inversion of surface subsidence of the Shadunzi Coal Mine in Hami City, Xinjiang[J]. Remote Sensing for Natural Resources, 2021, 33(3): 194-201.
[3] DU Cheng, LI Delin, LI Genjun, YANG Xuesong. Application and exploration of dissolved oxygen inversion of plateau salt lakes based on spectral characteristics[J]. Remote Sensing for Natural Resources, 2021, 33(3): 246-252.
[4] XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[5] LI Li, HU Xiao, PENG Jun. Fog removal effect optimization of aerial image based on dark channel prior[J]. Remote Sensing for Land & Resources, 2021, 33(1): 108-114.
[6] Zhenyu MA, Bowei CHEN, Yong PANG, Shengxi LIAO, Xianlin QIN, Huaiqing ZHANG. Forest fire potential forecast based on FCCS model[J]. Remote Sensing for Land & Resources, 2020, 32(1): 43-50.
[7] Chong YANG, Guoxiang LIU, Bing YU, Bo ZHANG, Rui ZHANG, Xiaowen WANG. Inversion of reservoir parameters in Shuguang Oil Production Plant of the Liaohe Oilfield based on InSAR deformation[J]. Remote Sensing for Land & Resources, 2020, 32(1): 209-215.
[8] Honge FENG, Jiaguo LI, Yunfang ZHU, Qijin HAN, Ning ZHANG, Shufang TIAN. Synergistic inversion method of chlorophyll a concentration in GF-1 and Landsat8 imagery: A case study of the Taihu Lake[J]. Remote Sensing for Land & Resources, 2019, 31(4): 182-189.
[9] Jun LI, Heng DONG, Xiang WANG, Lin YOU. Reconstructing missing data in soil moisture content derived from remote sensing based on optimum interpolation[J]. Remote Sensing for Land & Resources, 2018, 30(2): 45-52.
[10] Kun LU, Qingyan MENG, Yunxiao SUN, Zhenhui SUN, Linlin ZHANG. Estimating leaf area index of wheat at the booting stage using GF-2 data: A case study of Langfang City,Hebei Province[J]. Remote Sensing for Land & Resources, 2018, 30(1): 196-202.
[11] Chungui ZHANG, Bingqing LIN. Application of FY-2E data to remote sensing monitoring of sea fog in Fujian coastal region[J]. Remote Sensing for Land & Resources, 2018, 30(1): 7-7.
[12] MA Huiyun, ZHAO Guoqing, ZOU Zhengrong, ZHANG Weikang. Verification of the retrieval algorithm and analysis of influencing factors of fog physical parameters based on MODIS data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 122-128.
[13] XU Ao, MA Baodong, LI Xingchun, WU Lixin. Spectral testing and quantitative inversion for dust of iron tailings on leaf[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 164-169.
[14] YU Junchuan, LIU Wenliang, YAN Bokun, DONG Xinfeng, WANG Zhe, LI Na. Inversion of geochemical compositions of basalts based on field measured spectra[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 158-163.
[15] HAN Liang, DAI Xiaoai, SHAO Huaiyong, WANG Hongyan. An improved method for atmospheric transmissivity inversion based on field atmospheric modes[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 88-92.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech