An IQE Criterion-Based Method for SAR Images Classification Network Pruning

Jielei Wang, Zongyong Cui, Zongjie Cao, Hanzeng Wang, Changjie Cao

Abstract: Deep convolutional neural networks (DCNNs) have been widely used for SAR image target recognition. However, the huge demands of DCNNs for computing, storage, and energy resources limit their use on edge computing devices. In this article, we propose a method based on image quality evaluation (IQE) criterion to prune deep neural networks. We use IQE criterion to identify unimportant filters, and then remove them, to obtain a lightweight network while maintaining the performance of the neural network as much as possible. Besides, we verified the effectiveness of our method on the MSTAR dataset with cheap edge computing devices.