![]() Then, the nonlinear classifier is used for the SAR ATR, e.g.,, and its performance is better than the conventional template-based approaches. Beyond that, a Learning Vector Quantization (LVQ) has been used for learning the templates for classification in. To improve the performance, a number of methods called correlation pattern recognition have been presented, and they accomplish the training of the filter through minimizing the correlation between the filter and the spectral envelope of the training set in the frequency domain. So the addition of an object requires creating an additional set of templates, thus causing burdensome calculation. Then the identity of the test sample will be assigned to the class to which the matched template selected by the “distances” belongs. The methodology usually defines “distances” between the test sample and the templates generated by the training set. ![]() Firstly, template matching-based algorithms are utilized to achieve classification. Despite that, many works have been done in researching SAR. The SAR images contain coherent speckle noise, which lowers the images quality significantly, so it is very difficult to interpret them. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed. Experimental results demonstrate the good performance of SRC. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Then the sparse representation is solved by l 1-norm minimization. Specifically, a preprocessing method is recommended to extract the feature vectors of the image, and the feature vectors of the test samples can be represented by the sparse linear combination of basis vectors generated by the feature vectors of the training samples. Before the classification, the sizes of the images need to be normalized to maintain the useful information, target and shadow, and to suppress the speckle noise. In this paper, SRC is utilized to classify a 10-class moving and stationary target acquisition and recognition (MSTAR) target, which is a standard SAR data set. The framework, Sparse Representation-based Classification (SRC), has been widely applied as a classifier in numerous domains, among which Synthetic Aperture Radar (SAR) target recognition is really challenging because it still is an open problem to interpreting the SAR image. Recent years have witnessed an ever-mounting interest in the research of sparse representation.
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