High-resolution remote sensing image scenes have many categories and high similarities between categories.There are difficulties in distinguishing similar features and improving running speed.A fast image classification network based on multi-scale and partial convolution is proposed in this paper.First.,this method extracts target features of different scales through ordinary convolutions of different kernel sizes to achieve the fusion of features of different scales;second,adds a channel attention branch with weights to the spatial attention to adjust the location of the target position information.The channel weight expands the network width and strengthens the ability to locate the salient features of important targets;then,the 3×3 ordinary convolution in the Inception network is replaced with a 3×3 partial convolution to construct a multi-scale partial convolution module(MPCRes block),widens the width of the ResNet block and reduces the number of model parameters,improving network performance;finally,the MPCRes block and FasterNet block are cross-stacked to build a deeper network,which is continuously processed through convolution and pooling layers Downsampling until the extracted features are input to the Softmax classifier for classification.Experimental comparisons between this method and other deep learning-based methods were conducted on two data sets of AID and VGoogle with different training ratios.The results show that the parameter amount of the proposed model is 18.61 M,and the FLOPs are 2.58 G,in two different ratios in the AID data set The classification accuracy reached 97.35％and 98.99％respectively under the training samples,and reached 94.09％and 97.21％respectively under the two different proportions of training samples in the VGoogle data set.The overall effect is better than the comparison model,which reflects that the model is more effective in remote sensing scene classification tasks advantages.
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