Abstract: Remote sensing image classification is one of the key directions of remote sensing image information processing,and its classification accuracy greatly limits the overall development of remote sensing technology.Traditional machine learning algorithms and model structures have the disadvantages that they cannot quickly extract feature maps from remote sensing images,and the classification results are not accurate enough.Aiming at this problem,an improved model based on the ResNeXt network model combined with the attention mechanism is proposed to replace the fully connected layer model with the optimized SVM(support vector machine) algorithm.Firstly,it introduces the attention mechanism in computer vision,assigns different weights to different features,improves the ability to extract effective information for the classification part of the image,then combines the ResNeXt network,and finally replaces the end of the convolutional neural network with the optimized SVM algorithm.The fully connected layer is used to improve the classification effect,and at the same time optimize the network performance without increasing the hyperparameters of the model as a whole.Experimental results of the network model on the data set AID show that the improved network model has a significant improvement in the ability to extract deep features,and the optimized network mo-del has a better classification effect for multi-classification tasks.

Key words: Remote sensing image, CNN, ResNeXt, Attention mechanism, Scene classification, SVM