Abstract
Recent years have witnessed an ever-mounting interest in the research of sparse representation. 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. 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. 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. 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. Then the sparse representation is solved by l 1 -norm minimization. Finally, the identities of the test samples are inferred by the reconstructive errors calculated through the sparse coefficient. Experimental results demonstrate the good performance of SRC. Additionally, the average recognition rate under different feature spaces and the recognition rate of each target are discussed.
Highlights
Synthetic Aperture Radar (SAR), an active sensor, has been widely applied in many areas such as disaster assessment and military defense, due to its ability to work against 24 h a day and severe weather
With the high-resolution SAR coming to work, it is hard to carry on manual interpretation, making the Automatic Target Recognition (ATR) popular
Sparse Representation-based Classification (SRC) on three-class moving and stationary target acquisition and recognition (MSTAR) targets (BMP2, BTR70 and T72) is further studied in [8,9,10], which focuses on extracting effective features to improve the classification performance
Summary
Synthetic Aperture Radar (SAR), an active sensor, has been widely applied in many areas such as disaster assessment and military defense, due to its ability to work against 24 h a day and severe weather. The nonlinear classifier is used for the SAR ATR, e.g., [5], and its performance is better than the conventional template-based approaches. All these algorithms reduce their complexity by applying a set of classifiers trained by the training samples over a given range of aspect angles, so the performances of the algorithms are limited by the accuracy of aspect angle estimate. SRC on three-class MSTAR targets (BMP2, BTR70 and T72) is further studied in [8,9,10], which focuses on extracting effective features to improve the classification performance.
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