首页|A Grad-CAM and capsule network hybrid method for remote sensing image scene classification
A Grad-CAM and capsule network hybrid method for remote sensing image scene classification
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Remote sensing image scene classification and remote sensing technology applications are hot research topics.Although CNN-based models have reached high average accuracy,some classes are still misclassified,such as"freeway,""spare residential,"and"commercial area."These classes contain typical decisive features,spatial-relation features,and mixed decisive and spatial-relation features,which limit high-quality image scene classification.To address this issue,this paper proposes a Grad-CAM and capsule network hybrid method for image scene classification.The Grad-CAM and capsule network structures have the potential to recognize decisive features and spatial-relation features,respectively.By using a pre-trained model,hybrid structure,and structure adjustment,the proposed model can recognize both decisive and spatial-relation features.A group of experiments is de-signed on three popular data sets with increasing classifica-tion difficulties.In the most advanced experiment,92.67％average accuracy is achieved.Specifically,83％,75％,and 86％accuracies are obtained in the classes of"church,""palace,"and"commercial_area,"respectively.This research demonstrates that the hybrid structure can effectively improve performance by considering both decisive and spatial-relation features.Therefore,Grad-CAM-CapsNet is a promising and powerful structure for image scene classification.
image scene classificationCNNGrad-CAMCapsNetDenseNet
Zhan HE、Chunju ZHANG、Shu WANG、Jianwei HUANG、Xiaoyun ZHENG、Weijie JIANG、Jiachen BO、Yucheng YANG
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Shenzhen Data Management Center of Planning and Natural Resources,Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources),Shenzhen 518000,China
School of Civil Engineering,Hefei University of Technology,Hefei 230009,China
Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-restoration(Ministry of Natural Resources),Hefei 230088,China
State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
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open fund of the Key Laboratory of Jianghuai Arable Land Resources Protection and Ecorestoration(Ministry of Natural ResourcOpen Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation(Ministry of Natural Resources)
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