Aiming at the classic space targets, this paper discusses the reconstruction and feature extraction of HRR profiles by sparse component analysis.
针对典型的空间目标，文章采用稀疏成份分析法研究了一维距离像的超分辨重构和特征提取问题。
The proposed method is composed of the following three parts:(1) The feature space is reduced by the PCA(the principal component analysis) on the normalized input spectra;
该方法包括以下几个步骤：（1）先将训练样本归一化，再利用PCA变换进行降维，获得样本特征向量；
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