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Lei Miao - IEEE Xplore Author Profile

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Deep learning-based synthetic aperture radar (SAR) automatic target recognition (ATR) have shown great potential recently, however, the performance of these methods is subject to the number of annotated samples. In real application scenarios, it tends to acquire limited number of samples due to the acquisition cost, in which case the exising ATR method is susceptible to over-fitting. To achieve SA...Show More
Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods are developed for the closed-set environment, so these ATR methods can only identify known classes in the target library. However, it is known that the real scenario is open, so it requires the ATR model should be capable of identifying unknown categories while classifying known categories. Therefore, this paper propose...Show More
The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. The existing SAR ATR methods can achieve impressive results with the great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR...Show More