Abstract: The traditional pooling method will cause the loss of feature information, resulting in insufficient feature information extracted in the convolutional neural network. In order to improve the accuracy of the convolutional neural network in the image classification process and optimize its learning performance, based on the traditional pooling method, this paper proposes a double-pooling feature weighted structure pooling algorithm, using the maximum pooling and average pooling methods to retain more valuable feature information, and the model is optimized by genetic algorithm. By training convolutional neural networks with different pooling methods, the classification accuracy and convergence speed of convolutional neural networks on different data sets are studied. The experiments compare and verify the classification results of convolutional neural networks using several pooling methods on the remote sensing image data set NWPU-RESISC45 and the color image data set Cifar-10. The result analysis shows that the dual-pooling feature weighting structure makes the classification accuracy of the convolutional neural network be greatly improved, and makes the convergence speed of the model  be further improved.

Key words: convolutional neural network, double-pool, genetic algorithms, image classification