A Clustering Based System for Automated Oil Spill Detection by Satellite Remote Sensing

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• 关键词：Neural networks ; Synthetic Aperture Radar (SAR) ; Mahalanobis distance
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9693
• 期：1
• 页码：613-623
• 全文大小：2,092 KB
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• 作者单位：Giacomo Capizzi (19)
  Grazia Lo Sciuto (20)
  Marcin Woźniak (21)
  Robertas Damaševicius (22)
  
  19. Department of Electrical Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125, Catania, Italy
  20. Department of Engineering, Roma Tre University, Via Vito Volterra 62, Rome, Italy
  21. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100, Gliwice, Poland
  22. Software Engineering Department, Kaunas University of Technology, Studentu 50, Kaunas, Lithuania
  
• 丛书名：Artificial Intelligence and Soft Computing
• ISBN：978-3-319-39384-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9693

文摘

In this work a new software system and environment for detecting objects with specific features within an image is presented. The developed system has been applied to a set of satellite transmitted SAR images, for the purpose of identifying objects like ships with their wake and oil slicks. The systems most interesting characteristic is its flexibility and adaptability to largely different classes of objects and images, which are of interest for several application areas. The heart of the system is represented by the clustering subsystem. This is to extract from the image objects characterized by local properties of small pixel neighborhoods. Among these objects the desired one is sought in later stages by a classifier to be plugged in, chosen from a pool including both soft-computing and conventional ones. An example of application of the system to a recognition problem is presented. The application task is to identify objects like ships with their wake and oil slicks within a set of satellite transmitted SAR images. The reported results have been obtained using a back-propagation neural network.