| 一种基于改进的AdaBoost的SAR图像分类方法 |
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| 引用本文： | 杨胜智,温显斌,徐海霞.一种基于改进的AdaBoost的SAR图像分类方法[J].天津理工大学学报,2014(6):45-49. |
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| 作者姓名： | 杨胜智 温显斌 徐海霞 |
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| 作者单位： | 天津理工大学计算机与通信工程学院计算机视觉与系统教育部重点实验室天津市智能计算及软件新技术重点实验室,天津300384 |
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| 基金项目： | 国家自然科学基金（60872064,61102125）; 天津市自然科学基金（12JCYBJC10200） |
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| 摘 要： | 合成孔径雷达(SAR)图像由于受到相干斑点噪声的影响,使得其高精度的分类算法研究受到极大的挑战.为了提高SAR图像分类的性能,本文根据SAR图像的成像机理和统计特性,通过灰度共生矩阵特征的提取,结合纠错编码,构造了一种SAR图像分类的Ada Boost改进算法.实验结果表明,该算法得到较好的分类结果,分类精度得到了显著的提高.
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| 关 键 词： | AdaBoost 合成孔径雷达图像 分类 纠错编码 |
A classification method of SAR images based on improved AdaBoost |
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| YANG Sheng-zhi,WEN Xian-bin,XU Hai-xia.A classification method of SAR images based on improved AdaBoost[J].Journal of Tianjin University of Technology,2014(6):45-49. |
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| Authors: | YANG Sheng-zhi WEN Xian-bin XU Hai-xia |
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| Institution: | ( School of Computer and Communication Engineering, Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China) |
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| Abstract: | Owing to speckle noise, the research of high-precision classification of SAR （Synthetic Aperture Radar） images is a big challenge. According to the imaging mechanism and the statistical properties of SAR images, this paper proposes a clas- sification algorithm based on improved AdaBoost to improve the classification performance of SAR images. In this classification algorithm, the gray level co-occurrence matrix is used to extract the features and error correcting output code is introduced. Experimental results show that the proposed classification algorithm can obtain a better classification result and the accuracy is significantly improved. |
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| Keywords: | AdaBoost Synthetic Aperture Radar （SAR） classification Error Correcting Output Codes （ECOC） |
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