一种合成孔径雷达图像特征提取与目标识别的新方法
doi: 10.3724/SP.J.1146.2006.01198
A New Method for Synthetic Aperture Radar Images Feature Extraction and Target Recognition
-
摘要: 该文提出了一种利用小波域主成分分析和支持向量机进行的合成孔径雷达图像特征提取与目标识别的新方法。该方法对图像小波分解后提取低频子带图像的主成分分量作为目标的特征，利用支持向量机进行分类完成目标识别。实验结果表明，该方法可以明显提高目标的正确识别率，是一种有效的合成孔径雷达图像特征提取和目标识别方法。Abstract: This paper presents a new method for synthetic aperture radar images feature extraction and target recognition which based on principal component analysis in wavelet domain and support vector machine. After wavelet decomposition of a SAR image, feature extraction is implemented by picking up principal component of the low-frequency sub-band image. Then, support vector machine is used to perform target recognition. Results are presented to verify that, the correctness of recognition is enhanced obviously, and the method presented in this paper is a effective method for SAR images feature extraction and target recognition.
-
Sandirasegaram N and Englisth R. Comparative analysis offeature extraction (2D FFT and wavelet) and classification(Lp metric distances, MLP NN, and HNeT) algorithms forSAR imagery[J].Proc. SPIE.2005, Vol. 5808:314-325[2]Vapnik V N. An overview of statistical learning theory[J].IEEETrans. on Neural Networks.1999, 10(5):988-999[3]Smith L I. A tutorial on principal components analysis.Technique Report. Computer Vision, Department ofComputer Science, University of Otago, 26 February 2002.[4]Puyati W, Walairacht S, and Walairacht A. PCA in waveletdomain for face recognition. IEEE Conf. AdvancedCommunication Technology, Korea, 2006, Vol. 1: 450-455.[5]Safari M, Harandi M T, and Araabi B N. A SVM-basedmethod for face recognition using a wavelet PCArepresentation of faces. IEEE Conf. Image Processing,Singapore, 2004, Vol. 2: 853-856.[6]Zhao Q, Principe J C, and Brennan V L, et al.. Syntheticaperture radar automatic target recognition with threestrategies of learning and representation. Opt. Eng. 2000,39(5): 1230-1244.[7]Ross T D, Worrell S W and Velten V J, et al.. Standard SARATR evaluation experiments using the MSTAR public releasedata set[J].Proc. SPIE.1998, Vol. 3370:566-573[8]Zhao Q and Principe J C. Support vector machines for SARautomatic target recognition[J].IEEE Trans. on Aerospace andElectronic Systems.2001, 37(2):643-654 -
计量
- 文章访问数: 3119
- HTML全文浏览量: 81
- PDF下载量: 852
- 被引次数: 0
下载:
