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Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set

College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
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Academic Editor: Antonio Fernández-Caballero
Appl. Sci. 2016, 6(1), 26; https://doi.org/10.3390/app6010026
Received: 30 September 2015 / Revised: 25 December 2015 / Accepted: 8 January 2016 / Published: 20 January 2016
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. View Full-Text
Keywords: synthetic aperture radar; classification; sparse representation synthetic aperture radar; classification; sparse representation
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MDPI and ACS Style

Song, H.; Ji, K.; Zhang, Y.; Xing, X.; Zou, H. Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set. Appl. Sci. 2016, 6, 26.

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