| 摘要: |
| 溢油事故带来的海洋污染问题日益严重，SAR图像快速准确地自动识别为溢油事故的处理和决策支持提供了重要前提。为了获得更高的油膜识别准确率，提出了一种基于曲波变换（Curvelet）和局部线性嵌入（Local Linear Embedding,LLE）算法的SAR图像特征提取方法。首先，利用Curvelet对图像进行分解，选取包含了主要信息的低频分量作为新的图像矩阵；然后，利用LLE进行非线性降维，提取图像分类特征。为了验证提取特征的有效性，所提的Curvelet-LLE算法与PCA、LLE、等距特征映射（Isomap）、Curvelet变换和 Fisher 判别分析(Curvelet-KFD)、Wavelet-LLE等特征提取算法，利用K最近邻和支持向量机分类器分别进行了对比实验。实验结果表明，Curvelet-LLE算法能更有效地提取SAR图像油膜识别的分类鉴别特征，其准确率相对较高，具有较好的实用性。 |
| 关键词: SAR图像 油膜特征提取 曲波变换 局部线性嵌入(LLE) |
| DOI： |
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| 基金项目:国家自然科学基金资助项目(51609032) |
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| SAR image sea surface oil spill feature extraction using curvelet transform and local linear embedding algorithm |
| ZHOU Hui,CHEN Peng |
| (College of Computer and Software,Dalian Neusoft Information University,Dalian 116023,China;Navigation College,Dalian Maritime University,Dalian 116023,China) |
| Abstract: |
| The problem of marine pollution caused by oil spill accidents is becoming increasingly serious.The rapid and accurate automatic identification of synthetic aperture radar(SAR) images provides an important prerequisite for the handling and decision support of oil spill accidents.For the purpose of improving the precision rate of identification,a feature extraction method for SAR image is proposed based on the algorithm of Curvelet transform and local linear embedding(LLE).Firstly,the image is decomposed by Curvelet,and the low-frequency component containing the main information is selected as the new image matrix.Then the LLE is utilized for nonlinear dimensionality reduction for the data to extract the image classification features.In order to verify the validity of the extracted features,contrast experiments on the feature extraction algorithms such as PCA，LLE,Isomap,Curvelet-KFD and Wavelet-LLE are respectively carried on by the classifier of KNN and SVM.It turns out that the Curvelet-LLE can more effectively extract the features of oil spills identification in SAR image,with relatively higher accuracy and better practicability. |
| Key words: SAR image oil spill feature extraction curvelet transform locally linear embedding(LLE) |
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