Oil Spill Detection by SAR and Lidar system

Transcription

1 Oil Spill Detection by SAR and Lidar system Chaofang ZHAO Ocean Remote Sensing Institute Ocean University of China 11 November

2 Outline Introduction Oil spill detection by visible/infrared sensors Oil spill detection by Synthetic Aperture Radar Oil spill detection by Lidar Remarks

3 Introduction With the development of the economy and the increase of the energy consumption, accidental oil spills happen frequently from offshore oil platforms, pipe pp laying, ship collision or illegal discharge. According to the statistics, more than 2353 oil spill accidents occurred globally during Oil spill has very seriously impacts on marine environment, marine biology and marine economy especially on the people activities in the coastal area

4 With the rapid development of the satellite, remote sensing has become one of the most important and effective tools in oil spill detection such as the visible/infrared sensors (MODIS, FY), SAR, Lidar and etc. Oil spill detection by visible / Infrared sensors Oil spill detection by Synthetic Aperture Radar Oil spill detection by Laser / Lidar

5 The distribution of oil spill accidents (>700 tons)from 1970 to

6 Deepwater Horizon Accident happened in Mexico Gulf in Oil accident occurred in Dalian Bay in

7

8 p

9 Satellite Information Satellite Data Weather forecasting Satellite Data Center Satellite Data Analysis Ocean Numerical Center Prediction Data Meterological Center Weather Forcasting Remote Sensing Data Coordinating Ocean Information Monitoring Center Detection, recognization, tracking, prediction, information storage Emergency measure Emergency Center

10 (John L. Berry,2006) 10

11 Oil spill detection by visible/infrared sensors Physical basis: Visible The difference of reflectance between oil and sea water Infrared The difference of emissivity and absorption coefficient between oil and seawater Remote Sensing sensors: NOAA/AVHRR MODIS MERIS HY-1b FY-3 Ocean color sensors, Landsat, CBERS, IKONOS, Quickbirds, etc

12 Background W. Tseng, et al (1994) : Gulf oil pollution monitoring using NOAA/AVHRR data IGARSS 94 Otremba&Piskozub( ):Study the model of oil spill monitoring using optical method. Chuanmin Hu et al.(2003,2008,2009) ) used MODIS data to study optical properties, sun glitter of the oil and seawater; natural oil spill; Gulf of Mexico oil accident. Chust&Sagarminaga(2007)used MISR data to study oil spill in sun glitter area. Maria Adamo et al.(2009)found the oil spill could be detected in sun glitter area and it is not so easy to find the oil spill in other area.

13 The main problems to be resolved: Which spectral band suitable for oil spill monitoring? Which conditions (geometric or light) are better for oil spill monitoring? Data processing or other new theory?

14 NOAA/AVHRR data to study oil Spill monitoring ( 杨娜 & 赵朝方 2005) Object region Raw dara The possible oil feature Oil spill image Ratio, Differ ence Channel cal. stretch t RGB Image processing BW image Oil/water classified Reflectance, grey, amount relation Area, amount AVHRR data processing for oil spill

15 MODIS oil spill monitoring ( 陈辉 & 赵朝方 2008) Oil/water contrast:near-infrared>mid- t Infrared>Visible>Thermal Infrared The contrast of oil/water in reflectance band The contrast of oil/water in thermal band

16 FY3A/MERSI 美国 深水地平线 钻井平台溢油 ( 姜秋富 & 赵朝方 2010) The contrast of oil/water is increased around 30%- 120% after atmospheric correction, and the near infrared around 865nm is suitable for oil spill monitoring 沿横断面 3, 各通道大气校正后太阳耀斑反射率变化图 2010/04/

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18 The related published papers: 星载红外数据应用于大型事故溢油, 杨娜, 赵朝方, 地理空间信息, 2006 Oil spill detection by MODIS images using fuzzy cluster and texture feature extraction, IEEE Oceans 07, Aberdeen, UK, June 18 21, 2007(EI) by Lijian Shi, X. Zhang, G. Seielstad, M X He, C. Zhao MODIS 多光谱信息在海上溢油检测中的应用, 陈辉, 赵朝方, 海洋湖沼通报,2009 姜秋富, 赵朝方,2011, 基于风云三号 MERSI 数据的溢油检测方法研究, 海洋湖沼通报,4,pp

19 The existed problems: The mechanism of oil spill monitoring in visible and infrared spectral band. (geometric, light, sea states) Oil spill monitoring using multi-sensors (MODIS FY3A/MERSI HJ-1A/1B etc) The possibilities to retrieve the thickness of oil spill using optical sensor, the theoretical model?

20 Slicks appear lighter than clean seawater when viewed at a shallow angle and are invisible at steeper angles, except within the glint pattern from John L. Berry

21 The theory of SAR oil spill detection: Oil spill detection by SAR Oil spill changes water surface properties and causes wind wave aegenerating difficult. Rd Radar backscattering from sea surface with oil spill coverage is much lower compared with other oil spill free area

22 By damping the short surface waves and hence reducing the backscatter, the oil spills appeared as dark patches on SAR images. ERS-1 SAR image acquired on 20 May 1994 at 14:20 UTC over the Pacific Ocean east of Taiwan; orbit: 14874, frame: 2364, frame center: 23 01'N, 'E, imaged area: 100 km x 100 km. A northbound ship (bright spot at the front of the black line) was discharging g oil. The trail widened due to the dispersion of oil. (from Mitnik et al 2006)

23 IW Front Low wind Sea Ice Rain Cell

24 Research background: Pavlakis (1996) used ERS/SAR data to study oil spill distribution in Mediterranean sea Solberg et al. (1999) classified oil spill using statistical features and prior knowledge. Gade and Alpers (1999) found most of oil spill happened around main shipping lines using ERS-2/SAR data. Lu (1999) used around 5000 SAR images to study oil spill distribution in south east Asia seas. Fiscella et al.(2000) classified oil spill and natural features using Mahalanobis. Frate et al.(2000) used multilayer perceptron (MLP) neural network to classify oil spill features. Topouzelis et al. (2004) used RBF neural network to classify oil spill. Keramitsoglou et al. (2006) used fuzzy logic to give the possibility of oil spill

25 Method Author Comment Single threshold method Skoelv and Wahl(1993) Vachon et al(1998) Manore et al(1998) Adaptive threshold h Solberg et kd db below the mean value estimated method al(1999,2003) in a moving window. Hysteresis threshold method Laplace of Gaussian(LoG) operators Edge detection with wavelets method Hidden Markov Chain model Kanaa et al(2003) Change et al(1996) Chen et al(1997) Liu et al(1997) Wu and Liu(2003) Mercier et al(2003) Fuzzy classification Barm et al(1995) Fuzzy C-means algorithm Mathematical morphology Level Set method Gasull et al(2002) Huang et al(2005) Oil spills segmentation method

26 Oil spill detection system (software) Kongsberg Satellite Services (KSAT, Norway) has provided a service utilising satellite radar images for detection of oil spill since 1994, and has developed an oil detection service chain capable of providing information to European end users in near real time, i.e. less than one hour after satellite overpass; Ocean Monitoring Workstation (OMW) is Satlantic's (Canada) proven solution for maritime surveillance from Radarsat; BOOST Technologies (France) developed a powerful software designed to produce value-added information from SAR marine scenes acquired by various satellite missions (ERS, ENVISAT, RADARSAT): SARTool; PHOTOMOD Radar (RACURS, Russia) is intended for processing SAR data and generate the secondary information products; State Oceanic Administration developed an oil spill detection system based on ENVI

27 ERS-2 SAR image was acquired on 13 July 1997 at 02:27 UTC (image center at 6º07 N/115º12 E). Objects 1 and 2 were oil spills caused by oil discharged from ships, and objects 3 and 4 were look-alikes

28 (Shi Li Jian 2008)

29 Image preprocessing Image segmentation Feature extraction and filtering Oil spill recognition based on ANN Data processing flowchart

30 SAR image radiation correction (a) original (b) after correction SAR coherent noise removal, Enhanced Lee filter

31 Feature extraction From 27 ERS-2 images over China Sea, 129 objects was chosen to establish a dataset which included 77 oil spill samples and 52 look-like samples. Feature Feature Perimeter 14 Number of neighboring spots 2 Area 15 Homogeneity of surrounding 3 P/A 16 Ratio 6 to 5 4 Complexity (P 2 /A) 17 Ratio 8 to 7 5 Object area NRCS average 18 Ratio 5 to 7 6 Object area NRCS standard deviation 19 Ratio 6 to 8 7 Background area NRCS average 20 Ratio 19 to 18 8 Background area NRCS standard 21 Shape index deviation 9 Slick width 22 Spreading 10 Gradient 23 Normalized P to A 11 Gradient standard deviation 24 Distance to a point source 12 First invariant planar moment 13 Number of detected spots in the scene 1. Solberg(1999) scene 2. Espedal(1999) 4. Del Frate(1999) 3. Fiscella(1999)

32 Feature Filtering Feature ANOVA Feature ANOVA Feature ANOVA 1 Area Ratio background average to background SD 2 Perimeter E-5 13 Ratio 11 to Complexity E-8 14 Bounding box average Correlation Information measure 1 Information measure Object area average Second order moment Sum average Background average Third order moment Sum variance E-8 6 Object standard deviation Forth order moment Sum entropy E-5 7 Gradient Entropy Difference average 2E-4 8 Shape index E-6 19 Angular second moment Difference variance Ratio 4 to SD ratio of object and background 11 Rti Ratio 4t to Entropy (second order) Contrast 4E-4 Inverse difference moment E-4 31 Difference Entropy 2E ANOVA result of 31 features

33 Oil spill and the look-likes likes classification using neural network based on SAR data BP(Back Propagation) ) network Data sample: 77 oil spills and 52 look-likes from 27 ERS-2 SAR images; Training: 45 oil spills and 30 look-likes; Testing: 32 oil spills and 22 look-likes. Models Oil spills Look-likes Kappa coefficient Total agreement Model 1 Oil spills Look-likes 4 18 Model 2 Oil spills Look-likes

34 Oil spill distribution map Time: 2002~2005 Coverage: East China Sea; Data: more than 500 ERS-2 and Envisat SAR images were collected and finally 120 scenes containing oil spills were selected. SAR image Quick-look (200m) Statistical distribution in the East China Seas based on SAR image( 石立坚 & 赵朝方,2008) Full resolution (12.5m) ERS Envisat

35 Oil spill distribution map Four heavily polluted areas The central part of the Yellow Sea; The eastern area of Bohai Strait; The marine area in the vicinity of the mouth of the Yangtze River and surrounding waters; The southwestern part of the East China Sea and Taiwan Strait

36 Oil spill statistics Time statistics

37 Oil spill detection based on fuzzy logic theory (Liu Peng, Zhao Chaofang 2009) Fuzzification: Fuzzy sets are defined for input variables Inference: Min-max inference technique is used Synthesization: AND operation is used Defuzzification: The centroid defuzzification method is used Fuzzy rule base Fuzzification Fuzzy input Fuzzy inference Fuzzy synthesization Fuzzy output Defuzzification The main framework of fuzzy logic

38 ERS-2 SAR image was acquired on 13 July 1997 at 02:27 UTC (image center at 6º07 N/115º12 E). Objects 1 and 2 were oil spills caused by oil discharged from ships, and objects 3 and 4 were look-alikes

39 ERS 1 SAR image was acquired on 19 June 1995 at 02:31 UTC (image center at 35º37 N/122º35 E). Object 9 was the look alike phenomenon, the others were oil spills caused by oil platforms

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41 The related papers: Oil Spill Mapping in the Yellow Sea and East China Sea Using Synthetic Aperture Radar Imagery, International Journal of Remote Sensing by Lijian Shi, Andrei Yu. Ivanov, Mingxia HE and Chaofang Zhao, 2008 Texture feature application in oil spill detection by satellite data, 2008 Congress on Image and Signal Processing(EI) by Lijian Shi, Chaofang Zhao, 2008 基于 SAR 图像组合特征的海面溢油识别, 刘朋, 赵朝方, 石立坚,2008, 仪器仪表学报, 29(4), 基于纹理分析和人工神经网络的 SAR 图像中海面溢油识别方法, 石立坚, 赵朝方, 中国海洋大学学报,2009 基于模糊理论的 SAR 图像海面油膜识别, 李丹, 赵朝方, 刘朋, 海洋湖沼通报,2010 Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm,int, J. Remote Sensing(SCI)by Peng Liu, Chaofang Zhao,Xiaofeng Li,and Mingxia He, 2010 Peng Liu, Xiaofeng Li,John Qu, Wenguang Wang, Chaofang Zhao, and William Pichel,2011, Oil spill detection with fully polarimetric UAVSAR data. Mar. Pollut. Bull. (2011), doi: /j.marpolbul (SCI: 2.63)

42 Oil spill detection by Laser / Lidar

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44 The fluorescence of different oil type 44

45 Clean water Polluted water S P S R S D Heavily polluted water

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48 Intensity of Raman Spectra Relative thickness of oil spill

49 多通道海洋荧光激光雷达机载实验 (a) (b) (a) The airplane (b) The examination of the Lidar (c) The Lidar Installation (c) (d) The coaxial transmitter module (d)

50 The related published papers: 刘智深, 丁宁, 赵朝方等. 主成分分析法在油荧光光谱波段选择中的应用 [J]. 地理空间信息,2009,7(3): 齐敏珺, 赵朝方, 马佑军等. 机载激光荧光雷达油污染实时监测系统的软件开发 [J]. 计算机测量与控制,2010,18(7): 李晓龙, 赵朝方, 齐敏珺等. 多通道海洋激光雷达溢油监测系统高台实验分析 [J]. 中国海洋大学学报 ( 自然科学版 ),2010,8(40): 赵朝方, 李晓龙, 马佑军,2011, 多通道海洋荧光雷达溢油监测系统, 红外与激光工程,40(7), Li Xiaolong et al (2013), The fluorescence of oil spill detected by lidar and its in situ experiments, JOUC

51 Remarks The brief summaries about oil spill monitoring i using multi-sensors carried out in Ocean remote sensing institute, Ocean University of China are introduced. Many problems are still needed to be resolved: 1, Optical sensor optical features of oil spills show quite different. The theoretical explanations are still needed to be confirmed. Sun glitter, reflectance ratio? Oil spill thickness? 2, SAR data oil spill and the look likes classification? Automatically? what htkidif kind information can be retrieved ti dfrom multi-polarization l i SAR data? 3, Lidar detection oil type classification for main 4 to 5 types seems no problems at present time. The possibilities for oil thickness? It is very difficult to measure the thickness of oil spill at the laboratory at the present time

52 Thank you for your attentions!