| 溢油SAR图像分类中的纹理特征选择 |
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| 引用本文： | 梁小祎,张杰,孟俊敏. 溢油SAR图像分类中的纹理特征选择[J]. 海洋科学进展, 2007, 25(3): 346-354 |
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| 作者姓名： | 梁小祎 张杰 孟俊敏 |
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| 作者单位： | 大连海事大学,辽宁,大连,116026;国家海洋局,第一海洋研究所,山东,青岛,266061;国家海洋局,第一海洋研究所,山东,青岛,266061 |
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| 基金项目： | 国家海洋局青年海洋科学基金——溢油SAR遥感信息系统关键技术研究(2006401) |
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| 摘 要： | 针对海洋SAR图像的特点,采用基于灰度共生矩阵的纹理分析方法,提出适用于海洋溢油SAR图像分类的纹理特征量。并讨论了纹理特征量的筛选和纹理窗口大小的确定等问题。最后采用人工神经网络方法验证了SAR图象分类效果。
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| 关 键 词： | SAR 纹理分析 灰度共生矩阵 人工神经网络 |
| 文章编号： | 1671-6647（2007）03-0346-09 |
| 修稿时间： | 2006-07-17 |
Selection of Texture Characteristics in Classifying Oil Spill SAR Images |
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| LIANG Xiao-yi,ZHANG Jie,MENG Jun-min. Selection of Texture Characteristics in Classifying Oil Spill SAR Images[J]. Advances in Marine Science, 2007, 25(3): 346-354 |
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| Authors: | LIANG Xiao-yi ZHANG Jie MENG Jun-min |
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| Affiliation: | 1. Dalian Maritime University, Dalian 116026, China; 2. First Institute of Oceanography, SOA, Qingdao 266061, China |
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| Abstract: | According to the characteristics of ocean synthetic aperture radar(SAR) images,a texture analysis method based on grey level co-occurrence matrix is used,and the texture characteristic quantities suitable for the classification of ocean oil spill SAR images are suggested.The problems with the screening of texture characteristic quantities and the determination of texture window size are discussed,and the artificial neural network method is used to verify the classification results of SAR images. |
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| Keywords: | synthetic aperture radar(SAR) grey level co-occurrence matrix artificial neural network |
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