Combination of Statistical Similarity Measure and Derivative Morphological Profile Approach for Oil Slick Detection in SAR Images

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• 作者：Bahia Lounis ; Grégoire Mercier…
• 关键词：Statistical similarity measure ; Derivative morphological profile ; SAR image analysis ; Oil slicks detection
• 刊名：Journal of Mathematical Modelling and Algorithms in Operations Research
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：11
• 期：4
• 页码：409-432
• 全文大小：1,932 KB
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• 作者单位：Bahia Lounis (1)
  Grégoire Mercier (2)
  Aichouche Belhadj-Aissa (1)
  
  1. Laboratory of Image Processing and Radiation, Faculty of Electronics and Computer, University of Sciences & Technology of Houari Boumediene (USTHB), BP32 El Alia, Bab Ezzouar - Fac. d’Electronique & Informatique/LTIR, 16111, Alger, Algeria
  2. Institut Telecom, Telecom Bretagne - CNRS FRE 3167 lab-STICC, team CID. Technopole Brest-Iroise, CS 83818, 29238, Brest Cedex 3, France
  
• 刊物类别：Operations Research, Management Science; Optimization; Algorithms; Mathematical Modeling and Industr
• 刊物主题：Operations Research, Management Science; Optimization; Algorithms; Mathematical Modeling and Industrial Mathematics; Data Mining and Knowledge Discovery;
• 出版者：Springer Netherlands
• ISSN：2214-2495

文摘

Synthetic Aperture Radar (SAR) is widely used to detect and monitor oil pollution on the sea surface. As it is sensitive to surface roughness, the presence of oil film on the sea surface decreases the backscattering of this target type resulting in a dark feature patches in SAR images. In this paper, a new approach for oil slicks detection is presented. It is mainly based on SAR image texture analysis using the combination of a statistical similarity measure and a derivative morphological profile. Oil slicks signature is extracted trough two steps procedure. First, SAR image inspection is performed in order to highlight the dark spots suspected to be oil slicks. The inspection is achieved through a similarity measure between a local probability density function (lpdf) of clean water and the lpdf of the area to be inspected. The local distribution is estimated in the neighbourhood of each pixel and compared to a reference one using the Kullback-Leibler KL distance between distributions. Second, and once spots highlighted, texture features extraction using the Derivative Morphological Profile is porformed in order to improve the detection results. algorithm has been applied to Envisat Advanced Synthetic Aperture Radar (ASAR) and European Remote Sensing (ERS) images and it yields an accurate segmentation results. Indeed, the features extraction improves the detection slicks probability Pd of ASAR, respectively ERS, images from 93.08 %to 97.37 %and from 96.32 to 99.57 % on one hand, and reduces the false alarms probability respectively from 6.92 to 2.63 %and from 3.68 to 0.59 % on the other hand. Keywords Statistical similarity measure Derivative morphological profile SAR image analysis Oil slicks detection