海洋溢油合成孔径雷达图像特征提取及其关键度分析

韩吉衢; 孟俊敏; 赵俊生

doi:10.3969/j.issn.0253-4193.2013.01.010

海洋溢油合成孔径雷达图像特征提取及其关键度分析

doi: 10.3969/j.issn.0253-4193.2013.01.010

1.
烟台大学光电信息科学技术学院, 山东烟台264005
2.
国家海洋局第一海洋研究所, 山东青岛 266061
3.
国家海洋局第一海洋研究所, 山东青岛 266061;烟台大学环境与材料学院, 山东烟台 264005

基金项目: 国家海洋局海洋溢油鉴别与损害评估技术重点实验室(moidat)开放研究基金资助(201003)。

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出版历程
- 收稿日期: 2011-10-04
- 修回日期: 2012-06-03

Feature extraction and its criticality analysis for oil spill detection in synthetic aperture radar images

1.
Institute of Science and Technology for Opto-electronic Information, Yantai University, Yantai 264005, China
2.
First Institution of Oceanography, State Oceanic Administration, Qingdao, 266061, China
3.
First Institution of Oceanography, State Oceanic Administration, Qingdao, 266061, China;School of Environment and Material Engineering, Yantai University, Yantai 264005, China

摘要

摘要: 就SAR图像溢油检测的方法论而言,用于识别溢油和疑似现象的定性或定量的统计特征量选择,通常是任意的。对于不同的分类模型,所选用的特征量也不尽相同。主要是进行海洋SAR图像特征提取及其关键度分析。其目的是将"最小距离"判别法应用于海上溢油和疑似溢油的识别研究。首先,针对海洋SAR图像溢油检测常用的特征量,进行冗余处理;然后,引入关键系数,定量地研究特征量的关键度,提取显著特征量;藉以构造一个多维的特征矢量空间,以适于最小距离判别法在特征矢量空间中进行溢油和疑似溢油的识别研究。
- SAR图像 /
- 溢油 /
- 关键度 /
- 特征提取
Abstract: In terms of the methodology of oil spill identifying in SAR images, it is usually arbitrary to select qualitative or/and quantitative features for classifying dark objects as oil spill or look-alikes. The features selected in different classification models are not the same. The feature extraction and the criticality analysis are made in SAR images. Its aim is to apply the minimum distance method to discriminating oil spills from look-alikes. First, through correlation analysis, the redundancy is removed. Next,a criticality coefficient is introduced to quantitatively study the criticality of features. Then, distinguishing features are extracted. Sequentially, the dimension of feature vector is reduced to fit for the application research of the minimum distance method.
- synthetic aperture radar image /
- oil spill /
- criticality /
- feature extraction

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参考文献(15)

Jackson R, Apel R. Synthetic Aperture Radar Marine User's Manual[M]. NOAA, Sept, 2004.

Solberg A H, Storvik G, Solberg R V. Automatic detection of oil spills in ERS SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37:1916-1924.

Solberg A H S, Brekke C, Husoy P. Oil spill detection in Radarsat and Envisat SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 746-755.

Solberg A H S, Dokken S T, Solberg R. Automatic detection of oil spills in Envisat, Radarsat and ERS SAR images [G]// Proc IGARSS' 03, No.4.2003: 2747-2749.

Frate F D, Petrocchi A, Lichtenegger J, et al. Neural networks for oil spill detection using ERS2 SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5):2282-2287.

Fiscella B, Giancaspro A, Nirchio F, et al. Oil spill detection using marine SAR images[J]. International Journal of Remote Sensing, 2000(21):3561-3566.

Keramitsogloua I, Cartalisa C, Kiranoudis C T. Automatic identification of oil spills on satellite images[J]. Environmental Modeling and Software, 2006(21): 640-652.

Nirchio F, Sorgente M, Giancaspro A, et al. Automatic detection of oil spills from SAR images[J]. International Journal of Remote Sensing, 2005(26): 1157-1174.

Brekke C, Solberg A H S. Oil spill detection by satellite remote sensing[J]. Remote Sensing of Environment, 2005,95(1): 1-13.

Girard-Ardhuin F, Mercier G, Collard F,et al. Operational oil-slick characterization by SAR imagery and synergistic data[J]. IEEE Journal of Oceanic Engineering, 2005,30(3): 487-495.

Karathanassi V, Topouzelis K, Pavlakis P,et al. An object-oriented methodology to detect oil spills[J]. International Journal of Remote Sensing, 2006,27(23):5235-5251.

Topouzelis K, Stathakis D, Karathanassi V. Investigation of genetic algorithms contribution to feature selection for oil spill detection[J]. International Journal of Remote Sensing, 2009,30(3): 611-625.

Topuzelis K N. Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms[J]. Sensors, 2008,8: 6642-6659.

袁礼海, 宋建社, 沈涛. 合成孔径雷达图像特征关键度分析与分类算法研究[J]. 兵工学报, 2007, 28(10):1186-1190

Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems,Man and Cybernetics, 1973,SMC-3(6): 610-621.

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