Title: Optimal dense convolutional network model for image classification in unmanned aerial vehicles based ad hoc networks
Authors: I.S. Hephzi Punithavathi; S. Dhanasekaran; P. Duraipandy; E. Laxmi Lydia; M. Sivaram; K. Shankar
Addresses: Department of Computer Science and Engineering, Sphoorthy Engineering College, Hyderabad, India ' Department of Computer Science and Engineering, Kalasalingam University, Srivilliputtur, India ' Department of Electrical and Electronics Engineering, J.B. Institute of Engineering and Technology, Hyderabad, India ' Computer Science and Engineering, Vignan's Institute of Information Technology (Autonomous), India ' Department of Computer Networking, Lebanese French University, Erbil, Kurdistan Region, Iraq ' Department of Computer Applications, Alagappa University, Karaikudi, India
Abstract: Unmanned aerial vehicles (UAVs) have the potential of generating an ad hoc communication network on the fly. Aerial image classification gains more importance in the remote sensing community and several studies have been carried out in recent days. This paper presents an optimal dense convolutional network (DenseNet) with bidirectional long short term memory (Bi-LSTM) based image classification model called optimal DenseNet (ODN)-BiLSTM for UAV based adhoc networks. DenseNet model is applied as a feature extractor, where the hyperparameters of DenseNet are tuned by the use of Adagrad optimiser. Secondly, the Bi-LSTM model is applied as a classifier, which classifies the aerial images captured by UAV. Detailed performance analysis of the proposed model takes place using UCM aerial dataset and the results are investigated under several dimensions. The ODN-BiLSTM model has provided effective image classification results with the maximum accuracy of 98.14% and minimum execution time of 80s.
Keywords: ad hoc networks; deep learning; image classification; unmanned aerial vehicle; UAV.
International Journal of Ad Hoc and Ubiquitous Computing, 2022 Vol.39 No.1/2, pp.46 - 57
Received: 09 Jun 2020
Accepted: 31 Aug 2020
Published online: 18 Feb 2022 *