摘要
Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,etc.Besides,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV communication.At the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC systems.In UAV aerial imagery,efficient image representation is crucial for the purpose of scene classification.The existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average results.Therefore,the current research work introduces a new DL-enabled aerial scene classificationmodel forUAV-enabledMECsystems.The presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further processing.Next,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial image.It is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration errors.In order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is implemented.Finally,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial images.The performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS dataset.The proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.
