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The traditional multi-view synthetic aperture radar (SAR) automatic target recognition (ATR) is based on the assumption of viewpoint integrity of the dataset, requiring that the viewpoint information of all samples participating in the recognition is complete. To address the problem that SAR ATR is difficult under the training condition of an incomplete view sample set, a new cross-view SAR target recognition method based on view correlation extension and multi-task learning is proposed. To mine the complementary information between multi-view SAR images, the training datasets are clustered into subsets of different views based on the correlation coefficient. To improve the accuracy of cross-view recognition, we adopt the strategy of multi-view subsets corresponding to multi-task kernel sparse representation. For the decision values of each view subset, the fusion error is obtained by linear weighting. Finally, the total reconstruction errors accumulated from all tasks are identified. Experimental results on the MSTAR database in cross-view conditions show that our method outperforms the widely-used SAR ATR methods in several performance indicators.

Cross-view SAR target recognition based on multi-view correlation and multi-task architecture