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MULTI-LOOK SAR ATR USING TWO-LEVEL DECISION FUSION OF NEURAL NETWORK AND SPARSE REPRESENTATION

By X. Li, C.-S. Li, and P. Wang

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Abstract:
As for the lack of the contribution by decision fusion in pose estimation and the demand for the combination of the feature fusion and the decision fusion in SAR ATR, in this paper, with the help of pose estimation, a new multi-look SAR ATR method is proposed in order to improve the performance, which is based on two-level decision fusion of neural network and sparse representation. The first-level decision fusion is acted for the combination of the pose estimation result by neural network and sparse representation. Based on the constraint of pose, these two models are exerted for the multi-look SAR ATR, and the second-level decision fusion is used to achieve the final recognition result. Several experiments based on MSTAR are conducted, and experimental results show that our method can achieve an acceptable result.

Citation:
X. Li, C.-S. Li, and P. Wang, "Multi-Look SAR ATR Using Two-Level Decision Fusion of Neural Network and Sparse Representation," Progress In Electromagnetics Research M, Vol. 46, 89-100, 2016.
doi:10.2528/PIERM15092304

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