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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 111-117     DOI: 10.6046/gtzyyg.2019.02.16
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Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method
Xuewen XING1,Song LIU1,Degang XU2,Kaijun QIAN1
1.Petrochina Research Institute of Petroleum Exploration and Development, Beijing 100083, China
2.Chinese Petroleum Safety and Environmental Protection Technology Research Institute, Beijing 102206, China
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Abstract  

Thickness of oil slick is an important parameter of oil spill volume. In order to confirm the feasibility of oil thickness estimation with hyperspectral data,the authors used ASD FieldSpec3, quartz halogen lamp and crude oil for a laboratory experiment which simulates oil slick and spectral measurement. 27 pairs of oil thickness and reflection data were acquired. To make full use of spectral information of the hyperspectral data,the authors selected partial least square (PLS) to slick thickness and reflection modeling with 21 set model data and 6 test data set. Model result shows that PLS model expresses optimal effect when five principal components are selected which interpret 74% information of independent variables and 99.8% information of dependent variable, the prediction capability of the model runs up to 92.8%. The root mean squared error is 0.01 for modeling samples and 0.04 for validation samples. The PLS model shows better accuracy of modeling and validation error compared with traditional model, and thus it can be used in oil slicks thickness modeling with hyperspectral data.

Keywords oil slick simulation experiment      thickness of oil slicks      hyperspectral data      partial least square     
ZTFLH:  TP79  
Issue Date: 23 May 2019
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Xuewen XING
Song LIU
Degang XU
Kaijun QIAN
Cite this article:   
Xuewen XING,Song LIU,Degang XU, et al. Thickness estimation of crude oil slicks by hyperspectral data based on partial least square regression method[J]. Remote Sensing for Land & Resources, 2019, 31(2): 111-117.
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http://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.16     OR     http://www.gtzyyg.com/EN/Y2019/V31/I2/111
Fig.1  Sketch map of oil slicks measurement
Fig.2  Computational method of oil slick thickness
Fig.3  Reflection spectra of crude oil slicks
Fig.4  Linear correlation matrix
Fig.5  Plane graph of t1-t2 and T2 ellipse
Fig.6  Determination of the principal component number
主成分
分量
X
释能力
X累积
解释能力
Y
释能力
Y累积
解释能力
预测
能力
累积预
测能力
PC1 32.6 32.6 49.0 49.0 33.1 33.1
PC2 22.1 54.7 20.3 69.3 17.0 44.5
PC3 10.0 64.7 20.6 89.9 40.6 67.0
PC4 6.2 71.0 7.6 97.5 48.6 83.0
PC5 3.1 74.0 2.3 99.8 57.8 92.8
Tab.1  Explanation ability of each principle component(%)
Fig.7  Correlation of component t and component u
Fig.8  Relationship between measured and predicted oil slick thickness
Fig.9  Importance indexes of the independent variable factors of PLS model
Fig.10  w*c1-w*c2 map of component t1and t2
Fig.11  Curve fitting of oil slick’s thickness and reflection
模型 自变量 方程 RMSE
建模样本 验证样本
PLS R350R2 500 0.01 0.04
单波段指数模型 R1 086 Y=1.729 4 e-4048x 0.11 0.15
Tab.2  Comparison of different models
[1] 宋莎莎, 安伟, 李建伟 , 等. 海上溢油量评估方法研究综述[J]. 海岸工程, 2017,36(1):83-88.
doi: 10.3969/j.issn.1002-3682.2017.01.009 url: http://www.cnki.com.cn/Article/CJFDTOTAL-HAGC201701010.htm
[1] Song S S, An W, Li J W , et al. Review on the methods for assessment of marine oil spill volume[J]. Coastal Engineering, 2017,36(1):83-88.
doi: 10.3969/j.issn.1002-3682.2017.01.009 url: http://www.cnki.com.cn/Article/CJFDTOTAL-HAGC201701010.htm
[2] 安超 . 光学遥感溢油检测机理及实例分析[D]. 青岛:中国海洋大学, 2013.
[2] An C . The Mechanism of Optical Remote Sensing in Oil Spill Detection and Case Studies[D]. Qingdao:Ocean University of China, 2013.
[3] 赵冬至, 丛丕福 . 海面溢油的可见光波段地物光谱特征研究[J]. 遥感技术与应用, 2000,15(3):160-164.
doi: 10.3969/j.issn.1004-0323.2000.03.005 url: http://www.cnki.com.cn/Article/CJFD2000-YGJS200003004.htm
[3] Zhao D Z, Cong P F . The research of visual light wave-band feature spectrum of sea-surface oil spill[J]. Remote Sensing Technology and Application, 2000,15(3):160-164.
doi: 10.3969/j.issn.1004-0323.2000.03.005 url: http://www.cnki.com.cn/Article/CJFD2000-YGJS200003004.htm
[4] 刘旭拢, 邓孺孺, 秦雁 , 等. 水面浮油光谱测量及光谱特征分析[J]. 海洋科学, 2016,40(10):63-70.
doi: 10.11759/hykx20160118001 url: http://www.cnki.com.cn/Article/CJFDTotal-HYKX201610009.htm
[4] Liu X L, Deng R R, Qin Y , et al. Spectral measurement and characteristic analysis of an oil film floating above water[J]. Marine Sciences, 2016,40(10):63-70.
doi: 10.11759/hykx20160118001 url: http://www.cnki.com.cn/Article/CJFDTotal-HYKX201610009.htm
[5] 臧影 . 高光谱溢油图像波段选择在油膜厚度估算中的应用[D]. 大连:大连海事大学, 2010.
[5] Zang Y . Application of Hyperspectral Band Selection in Detection of Oil Slick Thickness[D]. Dalian:Dalian Maritime University, 2010.
[6] 兰国新 . 海上溢油遥感光谱信息挖掘与应用研究[D]. 大连:大连海事大学, 2012.
[6] Lan G X . Study on Spectral Information Mining and Application for Oil Spill Remote Sensing Monitoring[D]. Dalian:Dalian Maritime University, 2012.
[7] 肖剑伟, 田庆久 . 基于生物光学模型的水面薄油膜厚度的高光谱遥感反演实验研究[J]. 光谱学与光谱分析, 2012,32(1):183-187.
url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ120220000134C9FbHe
[7] Xiao J W, Tian Q J . Experimental study of offshore oil thickness hyperspectral inversion based on bio-optical model[J]. Spectroscopy and Spectral Analysis, 2012,32(1):183-187.
url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ120220000134C9FbHe
[8] 孙鹏, 宋梅萍, 安居白 . 基于光谱曲线响应特性的油膜厚度估计模型分析[J]. 光谱学与光谱分析, 2013,33(7):1881-1885.
url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ1309300001270FbIeL
[8] Sun P, Song M P, An J B . Study of prediction models for oil thickness based on spectral curve[J]. Spectroscopy and Spectral Analysis, 2013,33(7):1881-1885.
url: http://www.opticsjournal.net/Articles/Abstract?aid=OJ1309300001270FbIeL
[9] 刘丙新, 李颖, 张至达 , 等. 不同厚度海上油膜高光谱遥感波段敏感性研究[J]. 东北师大学报(自然科学版), 2015,47(4):156-160.
doi: 10.16163/j.cnki.22-1123/n.2015.04.032 url: http://www.cnki.com.cn/Article/CJFDTotal-DBSZ201504032.htm
[9] Liu B X, Li Y, Zhang Z D , et al. Study on the sensitivity of hyperspectral imagery to detect oil film with different thickness[J]. Journal of Northeast Normal University(Natural Science Edition), 2015,47(4):156-160.
doi: 10.16163/j.cnki.22-1123/n.2015.04.032 url: http://www.cnki.com.cn/Article/CJFDTotal-DBSZ201504032.htm
[10] Winkelmann K H . On the applicability of imaging spectrometry for the detection and investigation of contaminated sites with particular consideration given to the detection of fuel hydrocarbon contaminants in soil[D]. Berlin:BTU Cottbus, 2005.
[11] 高保彬, 潘家宇, 刘云鹏 , 等. 偏最小二乘法的煤层瓦斯含量预测模型研究[J]. 河南理工大学学报(自然科学版), 2015,34(2):146-150.
doi: 10.16186/j.cnki.1673-9787.2015.02.002 url: http://d.wanfangdata.com.cn/Periodical/jzgxyxb201502002
[11] Gao B B, Pan J Y, Liu Y P , et al. Study on prediction model of seam gas content based partial least squares regression[J]. Journal of Henan Polytechnic University(Natural Science), 2015,34(2):146-150.
doi: 10.16186/j.cnki.1673-9787.2015.02.002 url: http://d.wanfangdata.com.cn/Periodical/jzgxyxb201502002
[12] 李朋成, 朱军桃, 马云栋 , 等. 基于偏最小二乘法的近红外光谱分析应用[J]. 测绘地理信息, 2015,40(2):53-56.
doi: 10.14188/j.2095-6045.2015.02.015 url: http://www.cnki.com.cn/Article/CJFDTotal-CHXG201502017.htm
[12] Li P C, Zhu J T, Ma Y D , et al. Near infrared spectral analysis based on partial least squares[J]. Journal of Geomatics, 2015,40(2):53-56.
doi: 10.14188/j.2095-6045.2015.02.015 url: http://www.cnki.com.cn/Article/CJFDTotal-CHXG201502017.htm
[13] 刘忠华, 李云梅, 吕恒 , 等. 基于偏最小二乘法的巢湖悬浮物浓度反演[J]. 湖泊科学, 2011,23(3):357-365.
doi: 10.18307/2011.0307 url: http://d.old.wanfangdata.com.cn/Periodical/hpkx201103007
[13] Liu Z H, Li Y M, Lyu H , et al. Inversion of suspended matter concentration in lake Chaohu based on partial least squares regression[J]. Journal of Lake Sciences, 2011,23(3):357-365.
doi: 10.18307/2011.0307 url: http://d.old.wanfangdata.com.cn/Periodical/hpkx201103007
[14] 王慧文 . 偏最小二乘回归方法及其应用[M]. 北京: 国防工业出版社, 1999.
[14] Wang H W. Partial Least-Squares Regression Method and Applications[M]. Beijing: National Defense Industry Press, 1999.
[15] 杨杰, 方俊, 胡德秀 , 等. 偏最小二乘法回归在水利工程安全监测中的应用[J]. 农业工程学报, 2007,23(3):136-140.
doi: 10.3321/j.issn:1002-6819.2007.03.028 url: http://www.cnki.com.cn/Article/CJFDTotal-NYGU200703028.htm
[15] Yang J, Fang J, Hu D X , et al. Application of partial least-squares regression to safety monitoring of water conservancy projects[J]. Transactions of the Chinese Society of Agricultural Engineering, 2007,23(3):136-140.
doi: 10.3321/j.issn:1002-6819.2007.03.028 url: http://www.cnki.com.cn/Article/CJFDTotal-NYGU200703028.htm
[16] 杨鹏程 . 紫外吸收光谱结合偏最小二乘法海水硝酸盐测量技术研究[D]. 天津:国家海洋技术中心, 2013.
[16] Yang P C . Research on Determination of Nitrate in Seawater Based on Ultraviolet Spectra Combined with PLS Method[D]. Tianjin:National Ocean Technology Center, 2013.
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