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

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.

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