基于ICA和SVM的SAR图像特征提取与目标识别

提出一种利用独立分量分析和支持向量机的合成孔径雷达图像特征提取与目标识别方法。对图像小波分解后提取低频子带图像，对低频子带图像进行独立分量分析提取特征向量，利用支持向量机对特征向量分类完成目标识别。将该方法用于MSTAR数据中的3类目标识别，识别率最高可达96.92%。实验结果表明，该方法是一种有效的合成孔径雷达图像特征提取与目标识别方法。

SAR Images Feature Extraction and Target Recognition Based on ICA and SVM

This paper presents a new method for Synthetic Aperture Radar(SAR) images feature extraction and target recognition using independent component analysis and support vector machine. Low-frequency sub-band image is obtained by wavelet decomposition of a SAR image. Independent Component Analysis(ICA) is used for extracting feature vectors from the low-frequency sub-band image as the feature of the target. Support Vector Machine(SVM) is used to perform target recognition. The method is used for recognizing three-class targets in MSTAR database and the recognition rate arrives at 96.92%. Experimental result shows that the method is an effective method for SAR images feature extraction and target recognition.