基于小波域NMF特征提取的SAR图像目标识别方法

该文提出了一种基于小波域非负矩阵分解特征提取的合成孔径雷达图像目标识别方法。该方法对图像二维离散小波分解后提取低频子带图像，用非负矩阵分解对低频子带图像提取特征向量作为目标的特征，利用支持向量机进行分类完成目标识别。将该方法用于对MSTAR数据中三类目标识别，识别率最高可达97.51%，明显提高了目标的正确识别率。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。

Synthetic Aperture Radar Images Target Recognition Based on Wavelet Domain NMF Feature Extraction

This paper presents a method for synthetic aperture radar images target recognition based on wavelet domain non-negative matrix factorization feature extraction. Low-frequency sub-band image is obtained by 2-D discrete wavelet decomposition of a SAR image. Non-negative matrix factorization is used for extracting feature vectors from the low-frequency sub-band image as the feature of the target. Support vector machine is used to perform target recognition. The method is applied for recognizing three-class targets in MSTAR database and the highest correct probability of recognition arrives at 97.51% which is enhanced obviously. It is concluded that the method proposed in this paper is an effective method for SAR images feature extraction and target recognition.