Small Sample SAR Image Recognition Method Based on Central Loss Function
A supervision learning method based on the central loss function has been proposed to enhance the recognition performance of Synthetic Aperture Radar(SAR)images in small-sample sce-narios.This method involves learning category centers for each class and penalizing the distance be-tween the deep features of samples and their respective category centers,thereby improving both inter-class resolution and intra-class dispersity.To validate the effectiveness of this approach,it is compared with common deep learning algorithms on the MSTAR image recognition dataset.The experimental re-sults show that,compared to other deep learning models,this method exhibits more superior image recognition performance in scenarios with small samples.
synthetic aperture radarsmall sample image recognitioncenter loss functiondeep learning
万方数据
维普
