关键词: 遥感图像, 注意力机制, 卷积神经网络, 深度学习

Abstract: In order to improve the accuracy of convolutional neural network in the classification task of remote sensing image， a method based on multi-scale feature extraction for classification is proposed. Aiming at the problems that the scale difference of target objects in remote sensing images is large and ordinary convolutional extraction tends to produce redundant features， a multi-scale hybrid convolution module with ordinary convolution and atrous convolution is proposed， which can effectively enhance the feature extraction ability of the model. In terms of feature fusion， a feature cross fusion module is proposed， which can effectively fuse the semantic information of each branch and make full use of the information between various scale features for deep fusion and interaction. Aiming at the problem of complex land cover information in remote sensing images， a parallel attention module is proposed to apply attention mechanisms to each branch， which makes it pay more attention to the key parts of the image and ignore the redundant information. Compared with the existing methods， the classification performance of the proposed method is significantly improved. On the data sets WHU-RS19、RSSCN7 and SIRI-WHU，the overall accuracy of the proposed method reaches 98.19%、94.18% and 96.37%， respectively.

Key words: remotes sensing image, attention mechanism, convolutional neural network, deep learning