SAR target recognition using nonnegative matrix factorization with L1/2 constraint

Synthetic aperture radar automatic target recognition (SAR ATR) has been widely applied in both military and civil fields. Much work has been done to improve the performance of SAR ATR systems in which feature extraction is an important step. To obtain pattern feature in SAR ATR, Non-negative Matrix Factorization (NMF), which is a dimensionality reduction method, has been applied by some researchers, although without deeper investigation. Meanwhile, in the computer vision field, lots of researches have been done to improve NMF methods by enforcing sparse constraint with L₁-norm, like Non-negative Sparse Coding (NNSC), Local NMF (LNMF), and Sparse NMF (SNMF). Compared to L₁-norm, L_1/2-norm has been shown to have a more natural sparseness, however, little work has been done by using L_1/2-norm constraint to NMF. In this letter, we propose a novel variant of NMF with L_1/2 constraint, called L_1/2-NMF, and carry out a thorough study by applying it in SAR target recognition. After mathematical derivation and analysis, the update rules of proposed L_1/2-NMF are given in details. Experimental results on MSTAR public database show that both the basis and coding matrices obtained by L_1/2-NMF have higher sparseness than those obtained by NMF, NNSC and NMF with Sparseness Constraints (NMFsc). The recognition results demonstrate that the proposed L_1/2-NMF outperforms the other variants of NMF, like NNSC, NMFsc, and Nonsmooth NMF (NsNMF).