In this work, an improved principal component analysis method (IPCA) is proposed for SAR target recognition. Firstly, we use the sparse method to obtain the training samples which are most relevant to the test samples and their representation coefficients for the test samples. Then, using the principal component analysis (PCA) we obtain the optimal projection matrix so that different test samples after projection can be better classified by using the training sample information. The results of experiments, performed on SAR ground stationary targets based on the moving and stationary target acquisition and recognition (MSTAR) database, show that IPCA reaches higher recognition rate and better robustness to sparse aspect training samples of three true objects than PCA.
XIAO Yao
,
LIU Chang
. Improved PCA method for SAR target recognition based on sparse solution[J]. Journal of University of Chinese Academy of Sciences, 2018
, 35(1)
: 84
-88
.
DOI: 10.7523/j.issn.2095-6134.2018.01.011
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