电讯技术2016，Vol.56Issue(5)：495-500,6.DOI:10.3969/j.issn.1001-893x.2016.05.005
以非负矩阵分解提取局部特征的SAR目标稀疏表示分类
Sparse Representation Classification of SAR Targets with Local Features Extracted by Non-negative Matrix Factorization
张之光 1雷宏1
作者信息
- 1. 中国科学院 电子学研究所，北京100190
- 折叠
摘要
Abstract
Synthetic aperture radar( SAR) target classification is one of the core functions in automatic tar-gets recognition( ATR) system. It is essential in battle field surveillance,too. According to the characteris-tics that SAR images have prominent local scattering,it is proposed to perform non-negative matrix factori-zation( NMF) on the training samples to get low dimensional local encoding matrix,and subsequently per-form sparse representation classification( SRC) based on this encoding matrix. Processing results on real data of civilian vehicle targets in Gotcha project demonstrate that the proposed method outperforms other di-mension reduction methods such as down-sampling,random projection and principle components analysis, which are adopted with SRC. In this way,superiority of the method is revealed. Besides,the performances of SRC with and without non-negativity constraints are compared and analyzed by experiments. The experiment result reveals that SRC with non-negativity constraints leads to degradation of classification performance. In this way,it is unadvisable to include non-negativity constraint with regard to classification problem.关键词
合成孔径雷达/稀疏表示/目标分类/非负矩阵分解/局部特征提取Key words
synthetic aperture radar(SAR)/sparse representation/targets classification/non-negative ma-trix factorization(NMF)/local feature extraction分类
信息技术与安全科学引用本文复制引用
张之光,雷宏..以非负矩阵分解提取局部特征的SAR目标稀疏表示分类[J].电讯技术,2016,56(5):495-500,6.
