Optical remote sensing image classification is one of the key technologies in the field of Earth observa-tion.In recent years,researchers have proposed optical remote sensing image classification using deep neural net-works.Aiming at the problem of inadequate feature extraction in some network models,this paper proposes a remote sensing image classification method based on scene context perception and attention enhancement,called ScEfficient-Net.This method designs a scene context-driven module(SCDM)to model the spatial relationship between the target and its surrounding neighborhood,enhancing the original feature representation with scene context features.It intro-duces a convolutional block attention module(CBAM)to weight the feature maps based on the importance of channels and spatial locations,and combines it with a depth-wise separable convolution structure to extract discriminative infor-mation of the targets,referred to as ScMBConv.Based on the above works,the ScEfficientNet model,which incorpo-rates scene context perception and attention enhancement,is used for remote sensing image classification.Experimental results show that ScEfficientNet achieves an accuracy of 96.8％in AID dataset,which is a 3.3％improvement over the original network,with a parameter count of 5.55 M.The overall performance is superior to other image classification algorithms such as VGGNet19,GoogLeNet and ViT-B,confirming the effectiveness of the ScEfficientNet model.
维普
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