Optimal dense convolutional network model for image classification in unmanned aerial vehicles based ad hoc networks. (14th February 2022)

• Record Type: Journal Article
• Authors: Punithavathi, I.S. Hephzi; Dhanasekaran, S.; Duraipandy, P.; Lydia, E. Laxmi; Sivaram, M.; Shankar, K.
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 39:Number 1/2(2022)
• Page Start: 46
• Page End: 57
• Publication Date: 2022-02-14
• Subjects: ad hoc networks -- deep learning -- image classification -- unmanned aerial vehicle -- UAV; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

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.