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题名: A Region-based GLRT Detection of Oil Spills in SAR Images
作者: Lena Chang;Z.S. Tang;S.H. Changb;Yang-Lang Chang
贡献者: NTOU:Department of Communications Navigation and Control Engineering
关键词: Oil spills;SAR image;Image segmentation;Generalizes likelihood ratio test (GLRT);Constant false alarm ratio (CFAR)
日期: 2008-10-15
上传时间: 2011-10-21T02:35:48Z
出版者: Pattern Recognition Letter
摘要: abstract:In the study, we propose a fast region-based method for the 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 regions by using 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, we can partition the image into oil-polluted and sea reflection regions, respectively. Based on the segmentation results, we build data models of oil spills and approximate them by using normal distributions. Employing the built oil spills model and the generalized likelihood ratio test (GLRT) detection theory, we derive a closed form solution for oil spills detection. Our proposed method possesses a smaller variance and can reduce the confusion interval in decision. Moreover, we adopt the sample average of image region to reduce the computation complexity. The false alarm rate and oil spills detection probability of the proposed method are derived theoretically. Under the criterion of constant false alarm ratio (CFAR), we determine the threshold of the decision rule automatically. Simulation results performed on ERS2-SAR images have demonstrated the efficiency of the proposed approach.
關聯: 29(4), pp.1915-1923
显示于类别:[通訊與導航工程學系] 期刊論文


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