Target recognition in SAR images using sparse representation based on feature space
Gong Zhang · Journal of Chongqing University of Posts and Telecommunications · 2012
Pointing at the high dimension problem in SAR images' target recognition algorithm using sparse representation in image domain,we propose a new algorithm based on feature space after analyzing SAR images' statistical characteristic.First,the generalized 2-dimensional principal component analysis(G2DPCA) feature with low-dimension and high-precision is extracted to form an over-complete dictionary.Then,2D-Fisher linear discriminate criterion is used to optimize the dictionary,which makes correlation of atoms in the same class more compact and difference of atoms between classes more apart.Besides,optimization process cuts down complexity in sparse solving.Sparse representation coefficient of test sample is computed based on the optimal dictionary.Classification and recognition is realized according to the energy feature of coefficient.Experiment results based on MSTAR SAR image data show that,algorithm raised in this essay lowers complexity in sparse solving,and increases recognition accuracy and speed effectively within simple preprocessing of SAR images.