Abstract
Joint sparse representation (JSR) is mostly used in face recognition area. While in this paper, JSR is adopted in the area of SAR automatic target recognition (ATR). In our method, the whole training dictionary is divided into several sub-dictionaries, according to the label of training samples. And classification is based on the minimum representation error criterion. Independent and identically distributed (IID) Gaussian random projection is used to extract feature of SAR images. Experiments are carried out on moving and stationary target acquisition and recognition (MSTAR) public database. Experiments results show that recognition rates can be improved by our method, by combining more useful information and reducing interference information of target.
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Acknowledgement
This work is supported in part by the National Natural Science Foundation of China under Grants 61271287, 61371048, 61301265.
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Xu, L., Cao, Z. (2015). Sub-dictionary Based Joint Sparse Representation for Multi-aspect SAR Automatic Target Recognition. In: Mu, J., Liang, Q., Wang, W., Zhang, B., Pi, Y. (eds) The Proceedings of the Third International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-08991-1_18
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DOI: https://doi.org/10.1007/978-3-319-08991-1_18
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