基于特征概率函数的双阈值分割海面溢油检测
收稿日期: 2011-09-15
修回日期: 2012-07-14
网络出版日期: 2012-08-22
基金资助
null
Dual-threshold Oil Spills Detection Based on Characteristic Possibility Function
Received date: 2011-09-15
Revised date: 2012-07-14
Online published: 2012-08-22
Supported by
null
海面溢油对生态环境造成了严重危害,故及早发现和尽快处理对降低事故影响和经济损失起着至关重要的作用。合成孔径雷达(SAR)是观测海面溢油、快速检测和事故态势分析判断的有效技术途径。本文针对SAR图像的海面溢油检测,提出了一种特征概率函数的双阈值分割方法。首先,通过高低阈值分割提取不同层次的灰度信息,再利用密度估计提取灰度的空间分布信息,然后,通过构建概率函数对油膜和类油膜区域进行形态学分类,最后,结合辅助信息,获得最终的海面溢油检测结果。本文利用香港中文大学卫星地面站接收的ENVISAT ASAR图像开展实验,结果表明,本文提出的方法能够准确地排除由风场或者水流场导致的低散射区域,有效地检测和识别生成不久的中型油膜,从而有助于溢油事故的早期预警与处置。
李煜, 张渊智*, 陈杰 . 基于特征概率函数的双阈值分割海面溢油检测[J]. 地球信息科学学报, 2012 , 14(4) : 531 -539 . DOI: 10.3724/SP.J.1047.2012.00531
Oil spills can cause huge damage to the ecology of marine environment and its detection and clean-up plays a very important role in the reduction of economic and environmental losses. As an advanced technology, synthetic aperture radar (SAR) has the advantage of day-night all weather observation capability. Besides, SAR has relatively wide swath width and high resolution, which all helps a lot in the early warning and damage analysis of oil spills accidents. Due to the special imaging mechanism of SAR, oil spills can be found as dark spots in SAR images. However, there still remain a lot of difficulties in the related detection and classification algorithms. In this paper, a double-threshold oil spills detection based on characteristic possibility function was proposed for taking the best advantages of backscatter information contained in different grayscale levels. Both high and low levels of grayscale information were extracted from the backscatter image obtained from SAR signal. Then the density of pixels was evaluated by Gauss kernel to enhance the stability of the segmentation. By using high level segmentation result, look-alikes with large area are classified from oil spills by basic morphological analysis. By taking advantage of low threshold grayscale information, other look-alikes were distinguished from oil spills by means of probability likelihood function derived from morphological characters such as complexity, length to width ratio, Euler number, etc.. Finally, the detected spills were obtained by fusing classification result of different level and other auxiliary information. The proposed method was implemented on EVISAT ASAR images of coastal region around Hong Kong received by the satellite ground station, CUHK. Experimental results demonstrated that real oil spills and look-alikes generated by other natural phenomena such as low wind speed and internal water turbulence could be distinguished accurately and effectively. This method can be further developed and has potential use in the surveillance and early alarm of marine and coastal oil leak accidents.
Key words： SAR imagery; dark spot extraction; oil spill detection; classification
[1] Shu Y M, Li J, Yousif H. Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring[J]. Remote Sensing of Environment, 2010, 114(9):2026-2035.
[2] Migliaccio M, Gambardella A and Tranfaglia M. SAR polarimetry to observe oil spills[J]. IEEE Trans. Geosci. Remote Sens., 2007,45(2):506-511.
[3] Fan K, Zhang Y and Lin H. Satellite SAR analysis and interpretation of oil spill in the offshore water of Hong Kong[J]. Annals of GIS, 2010, 16(4): 269-275.
[4] Cheng Y C, Li X F, Xu Q. SAR observation and model tracking of an oil spill event in coastal waters[J]. Marine Pollution Bulletin, 2011(62): 350-363.
[5] 李四海. 海上溢油遥感探测技术及其应用进展[J]. 遥感信息,2004(2): 53-57.
[6] Solberg A H S, Storvik G, Solberg R and Volden E. Automatic detection of oil Spills in ERS SAR images[J]. IEEE Trans. Geosci. Remote Sens., 2009,37(4):1916-1924.
[7] Galland F, Réfrégier P and Germain O. Synthetic aperture radar oil spill segmentation by stochastic complexity minimization[J]. IEEE Trans. Geosci. Remote Sens., 2004,1(4):295-299.
[8] Mercier G and Girard-Ardhuin F. Partially supervised oil-slick detection by SAR imagery using kernel expansion[J]. IEEE Trans. Geosci. Remote Sens., 2006, 44(10): 2839-2846.
[9] Migliaccio M. A study on the use of SAR polarimetric data to observe oil spills[J]. Oceans-Europe, 2005.
[10] Migliaccio M, Nunziata F, Gambardella A. On the copolarised phase difference for oil spill observation[J]. Int. Journal of Remote Sensing, 2009,30(6):1587-1602.
[11] Wang G W, Zhang Y Z, Lin H. A study of oil spill detection using ASAR images[J]. Acta Oceanolofica Sinica, 2009, 28(4):32-37.
[12] Zhang Y Z, Lin H, Liu Q, et al. Oil-spill monitoring in the coastal waters of Hong Kong and vicinity[J]. Marine Geodesy, 2012,35:1-14.
[13] 马腾波,王思远. 基于边缘分析的海面溢油检测[J]. 遥感学报, 2009,13(6):1097-1091.
[14] 陈丹. 基于模糊理论的SAR图像海面油膜识别方法研究. 青岛: 中国海洋大学,2009.
[15] 原君娜,邵芸,田维. 利用SAR图像识别海面油膜的方法介绍[J].遥感技术与应用,2010,25(1):97-101.
[16] Touzi R, Lopes A, Bousquet P. A statistical and geometrical edge detector[J]. IEEE Trans. Geosci. Remote Sens.,1988, 26(6): 764-773.
[17] http://hk.szhk.com/2010/05/21/282749224516245.html.
[18] http://www.cpr.cuhk.edu.hk/sc/press_detail.php?1=1&id=780.
/
| 〈 |
|
〉 |