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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28126

Title: An Automatic Detection of Oil Spills in SAR Images by Using Image Segmentation Approach
Authors: Lena Chang;C. M. Cheng;Z. S. Tang
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Date: 2005-07-25
Issue Date: 2011-10-21T02:35:58Z
Publisher: Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International
Abstract: Abstract:In the study, we propose a region-based method for the automatic detection of oil spills in SAR images. The proposed method combines the image segmentation technique and conventional detection theory to improve the accuracy of oil spills detection. From the image statistical characteristics, we first segment the image into proper regions by using one-dimensional moment preserving method. Then, to get a more integrated segmentation result, we adopt N nearest neighbor rule to merge the image regions according to their spatial correlation. Performing the split and merge procedure recursively, we can partition the image into proper regions, oil-polluted and sea areas. Based on the segmentation results, then, we propose an oil spills detection algorithm, which involves data modeling of oil-polluted image data and the development of an automatic decision rule. Employing the built oil spills model and the generalized likelihood ratio (GLRE) detection theory, we derive a region-based decision rule for oil spills detection. Under the criterion of Constant False Alarm Ration (CFAR), we may determine the threshold automatically. Simulation results performed on ERS2-SAR images have demonstrated the efficiency of the proposed approach.
Relation: 2, pp.1021-1024
URI: http://ntour.ntou.edu.tw/handle/987654321/28126
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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