QFO-UCNN: Quantizable Feature Oriented Utilitarian Convolutional Neural Network for Sar Recognition

Synthetic aperture radar (SAR) imagery is susceptible to the non-systematic motion error, which will defocus the data in the training set. Also, traditional convolutional neural networks (CNN) are random in the features utilized, many of which are useless for practical target recognition. To this end, quantizable feature oriented utilitarian CNN (QFO-UCNN) is proposed in this paper, where the defocusing can be alleviated via a quantizable entropy norm regularizer. To overcome the non-convexity of the optimization function, a tight surrogate function is designed to ensure the closed-form solution of the focusing feature. Besides, the dominant feature of sparsity is encoded by a l
1
norm, where the sparse feature is quantified via a closed-form soft-thresholding operator. In such cases, two useful features are quantified and constrained in the CNN, which improves the intentionality of the intended SAR target recognition. The canonical MSTAR dataset is employed to verify the effectiveness of the proposed algorithm, when the data encounters the non-systematic and multiplicative phase error.