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Unsupervised noise-robust feature extraction for aerial image classification

Science China(Technological Sciences) 2020年第8期63卷 1406-1415页

作者： LIANG Ye LU Shuai WENG Rui HAN ChengZhe LIU Ming School of Astronautics Harbin Institute of TechnologyHarbin 150001China Academy of Art Harbin University of Science and TechnologyHarbin 150001China Academy of Software and Microelectronics Harbin University of Science and TechnologyHarbin 150001China

The rich data provided by satellites and unmanned aerial vehicles bring opportunities to directly model aerial image features by extracting their spatial and structural *** convolutional autoencoders(CAEs)have been attained a remarkable performance in ideal aerial image feature extraction,they are still challenging to extract information from noisy images which are generated from capture and *** this paper,a novel CAE-based noise-robust unsupervised learning method is proposed for extracting high-level features accurately from aerial images and mitigating the effect of *** from conventional CAEs,the proposed method introduces the noise-robust module between the encoder and the ***,several pooling layers in CAEs are replaced by convolutional layers with stride=*** performance of feature extraction is evaluated by the prediction accuracy and the accuracy loss in image classification experiments.A 5-classes aerial optical scene and a 9-classes hyperspectral image(HSI)data set are utilized for optical image and HSI feature extraction,*** features extracted from aerial images are utilized for image classification by a linear support vector machine(SVM)*** results indicate that the proposed method improves the classification accuracy for noisy images(Gaussian noise 2Dσ=0.1,3Dσ=60)in both optical images(2D 87.5%)and HSIs(3D 85.6%)compared with the traditional CAE(2D 78.6%,3D 84.2%).The accuracy loss in classification experiments increases with the increment of *** with the traditional CAE(2D 15.7%,3D 11.8%),the proposed method shows the lower classification accuracy loss in experiments(2D 0.3%,3D 6.3%).The proposed unsupervised noise-robust feature extraction method attains desirable classification accuracy in ideal input and enhances the feature extraction capability from noisy input.

关键词： aerial image classification convolutional autoencoder feature extraction noise-robust

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Archimedes Optimization with Deep Learning Based aerial image classification for Cybersecurity Enabled UAV Networks

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Computer Systems Science & Engineering 2023年第11期47卷 2171-2185页

作者： Faris Kateb Mahmoud Ragab Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The recent adoption of satellite technologies,unmanned aerial vehicles(UAVs)and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher ***,security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security *** study presents an Archimedes Optimization with Deep Learning based aerial image classification and Intrusion Detection(AODL-AICID)technique in secure UAV *** presented AODLAICID technique concentrates on two major processes:image classification and intrusion *** aerial image classification,the AODL-AICID technique encompasses MobileNetv2 feature extraction,Archimedes Optimization Algorithm(AOA)based hyperparameter optimizer,and backpropagation neural network(BPNN)based *** addition,the AODLAICID technique employs a stacked bi-directional long short-term memory(SBLSTM)model to accomplish intrusion detection for cybersecurity in UAV *** the final stage,the Nadam optimizer is utilized for parameter tuning of the SBLSTM *** experimental validation of the AODLAICID technique is tested and the obtained values reported the improved performance of the AODL-AICID technique over other models.

关键词： aerial image classification remote sensing intrusion detection cybersecurity deep learning

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