Abstract
Convolutional neural networks have achieved remarkable success in the field of computer vision. However, due to their high storage and expensive computations, recently, there has been a lot of work focusing on reducing the complexity of convolutional neural networks. In this work, we propose a random filter pruning method by means of evolutionary multiobjective optimization algorithms to accelerate the Siamese ResNet-50 for remote sensing scene classification. We have conduct experiments on NWPU-RESISC45, UC Merced Land-Use and SIRI-WHU datasets for performance evaluation of the proposed method. The experimental results demonstrate that the classification performance of our pruned model has been improved while keeping a certain degree of sparsity of the model.
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