Sea oil spill detection method by SAR imagery using object-based image analysis and fuzzy logic
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摘要: 星载合成孔径雷达(Synthetic Aperture Radar,SAR)具有全天时、全天候的工作能力,已被众多学者认为是非常适合探测海面溢油污染的遥感器。然而在SAR影像中经常出现"类油膜"现象,这严重干扰了SAR溢油检测的精度。因此,如何有效区分SAR影像中的油膜和类油膜,对提升溢油检测精度具有重要意义。本文利用面向对象图像分析的方法,从20景ENVISAT ASAR影像中提取了较多的溢油和类油膜样本,对其基于对象的形状、物理和纹理特征进行了综合分析,找出了适合区分溢油和类油膜的特征量。利用特征分析的结论,本文建立了一种基于模糊逻辑的溢油检测算法。该算法可以有效区分SAR影像中的溢油和类油膜,还可以给出暗斑被判定为溢油的概率。溢油检测实验说明,本文方法能够得到令人满意的效果。Abstract: Synthetic aperture radar (SAR),a sensor with all weather and day and night working capacity,has been widely considered as a powerful tool for sea surface oil spill detection. However,lookalikes frequently appear in SAR images,limiting the performance of SAR to detect oil spilled at sea. Thus it is important to study how to effectively differentiate oil spill from lookalike. By using Object-based Image Analysis (OBIA),a number of oil spill and lookalike samples are extracted from 20 scenes of ENVISAT ASAR images. The object-based geometric,physical and textural features of the samples are analyzed with the objective of determining the best feature variables for oil spill and lookalike separation. The conclusions derived from feature analysis are utilized for the construction of an FL-based oil spill classifier. The proposed method can effectively single out oil spill from lookalike,giving the crisp probability of a dark segment being oil spill at the same time. Oil spill detection experiment indicates that our method can produce satisfactory result.
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Key words:
- SAR /
- feature analysis /
- oil spill classification /
- OBIA /
- fuzzy logic
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