SAR target feature extraction based on sparse constraint nonnegative matrix factorization

Feature extraction is the key technology and the core task of Synthetic Aperture Radar (SAR) target recognition. In this paper, a new target feature extracting method based on Sparse Non-negative Matrix Factorization (SNMF) is presented, which mainly use SNMF as the method to decompose the SAR target image and to construct the sparse feature vector. By this means, the similarity inside each cluster of the feature vectors is improved and the difference between the clusters is also raised. An identification test using the classification method of Support Vector Machine (SVM) demonstrates that the proposed method, compared to PCA, ICA and the general NMF feature extraction methods, can improve the stability and the accuracy of the target recognition significantly.