SAR Target Configuration Recognition Using Locality Preserving Property and Gaussian Mixture Distribution

Feature extraction is the key step of synthetic aperture radar (SAR) target configuration recognition. A statistical model embedding the locality preserving property is presented to extract the maximum amount of desired information from the data, which is of crucial help to recognition. The noise, or error, of the SAR image samples is described by a Gaussian mixture distribution, and the locality preserving property is embedded into the statistical model to focus on the problem of configuration recognition. Along with the extraction of the information of interest through the use of the statistical model, also, the preservation of the local structure of the data set is achieved. Parameter estimation is implemented through the expectation-maximization algorithm. Experimental results on the Moving and Stationary Target Acquisition and Recognition data set validate the effectiveness of the proposed method. SAR target configuration recognition is realized with satisfactory accuracy.