基于双池化特征加权结构CNN的图像分类

传统的池化方式会造成特征信息丢失，导致卷积神经网络中提取的特征信息不足。为了提高卷积神经网络在图像分类过程中的准确率，优化其学习性能，本文在传统池化方式的基础上提出一种双池化特征加权结构的池化算法，利用最大池化和平均池化2种方式保留更多的有价值的特征信息，并通过遗传算法对模型进行优化。通过训练不同池化方式的卷积神经网络，研究卷积神经网络在不同数据集上的分类准确率和收敛速度。实验在遥感图像数据集NWPU-RESISC45和彩色图像数据集Cifar-10上对采用几种池化方式的卷积神经网络分类结果进行对比验证，结果分析表明：双池化特征加权结构使得卷积神经网络的分类准确率有很大程度的提高，同时模型的收敛速度得到进一步提高。

Image Classification Based on Double-pooling Feature Weighted Convolutional Neural Network

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.