Data Customization-Based Multiobjective Optimization Pruning Framework for Remote Sensing Scene Classification

Pruning techniques have been utilized widely for convolutional neural networks (CNNs) to reduce the computation resources in remote sensing scene image classification. However, conventional pruning techniques are weight-based, which cannot balance the pruning ratio and representation ability appropriately. In this article, we propose a data customization-based multiobjective optimization pruning (DCMOP) framework for the pruning in remote sensing scene image classification, which can not only tradeoff between pruning ratio and capability for CNNs but also speed up the evolutionary process for the pruning. We adopt the multiobjective evolutionary algorithms (MOEAs) to search for a tradeoff between the pruning ratio and capability for CNNs. However, a big concern of pruning for networks via MOEAs is that the evaluation of subnetworks is time-consuming. This originates that the slimmed subnetworks require a lot of retraining operation, which will burden the hardware. In order to alleviate this limitation, we design a data customization-based proxy mechanism (DCPM) to reduce the size of the input dataset in terms of the structure of the slimmed subnetwork to accelerate significantly the evolutionary process for the pruning. According to this, our proposed DCMOP achieves the pruning with higher efficiency and performance by cooperating with MOEAs and DCPM. Experimental results based on four datasets of AID, NWPURESISC45, PatternNet, and WHU-RS19 show that the proposed DCMOP can achieve a balance between model performance and pruning rate, while obviously reducing the time cost of the pruning.