| 一种有效的聚束式合成孔径雷达图像特征提取算法 |
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| 引用本文： | 傅雄军,高梅国,何媛.一种有效的聚束式合成孔径雷达图像特征提取算法[J].北京理工大学学报,2004,24(7):638-642. |
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| 作者姓名： | 傅雄军 高梅国 何媛 |
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| 作者单位： | 北京理工大学,信息科学技术学院电子工程系,北京,100081;北京理工大学,信息科学技术学院电子工程系,北京,100081;北京理工大学,信息科学技术学院电子工程系,北京,100081 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 提出一种聚束式合成孔径雷达图像特征提取的有效算法.通过小波变换图像去噪法提高信噪比;利用Canny算子完成边缘检测;根据雷达图像的特点提出边缘检测后不做曲线闭合,而直接进行阈值处理的图像分割.图像预处理后提取具有旋转、尺度、平移不变性的Hu矩作为特征矢量并归一化,在训练阶段引入聚类分析.以MSTAR实测数据为样本,用最近邻分类器和BP神经网络分类器对该特征提取算法进行识别能力测试,算法的有效性得到了验证.
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| 关 键 词： | 聚束式合成孔径雷达 自动目标识别 不变矩 特征提取 分类器 |
| 文章编号： | 1001-0645(2004)07-0638-05 |
| 收稿时间： | 9/9/2003 12:00:00 AM |
| 修稿时间： | 2003年9月9日 |
Effective Feature Extraction Algorithm for Spotlight Synthetic Aperture Radar Images |
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| FU Xiong-jun,GAO Mei-guo and HE Yuan.Effective Feature Extraction Algorithm for Spotlight Synthetic Aperture Radar Images[J].Journal of Beijing Institute of Technology(Natural Science Edition),2004,24(7):638-642. |
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| Authors: | FU Xiong-jun GAO Mei-guo and HE Yuan |
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| Institution: | Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China;Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | An effective algorithm of feature extraction for spotlight synthetic aperture radar images is presented. The signal noise ratio of the image is improved by denoising using wavelet transform, and edge detection is performed with the Canny operator. According to the characteristics of radar image, a method of image segmentation is suggested by performing threshold processing directly after edge detection instead of close curves. The Hu moments, which are rotation, scale and translation invariant, are extracted as feature vector and normalized after image preprocessing as mentioned above, and clustering analysis is applied in the training phase. The recognition capability of this feature extraction algorithm is tested with the MSTAR experimental data using both the nearest neighbor classifier and the back propagation neural network classifier, and the effectivity of this algorithm is validated. |
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| Keywords: | spotlight synthetic aperture radar automatic target recognition moment invariants feature extraction classifier |
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