relation: https://khub.utp.edu.my/scholars/13716/
title: Object detection and fuzzy-based classification using UAV data
creator: Qayyum, A.
creator: Ahmad, I.
creator: Iftikhar, M.
creator: Mazher, M.
description: UAV (Unmanned Aerial Vehicle) equipped with remote sensing devices can acquire spatial data with a relevant area of interest. In this paper, we have acquired UAV data for high voltage power poles, urban areas and vegetation/trees near power lines. For object classification, the proposed approach based on the fuzzy classifier is compared with the traditional minimum distance classifier and maximum likelihood classifier on our three defined segments of UAV images. The performance evaluation of all the classifiers was based on the statistics parameters which included the mean, standard deviation and PDF (probability density function) of each object present in the image acquired by the UAV and the variances of each channel of the UAV imagery were calculated. The results showed that the fuzzy-based classifier outperformed as compared to the other classifiers. We achieved the classification accuracy of 93 with a Fuzzy-based classifier. © 2020, Tech Science Press. All rights reserved.
publisher: Tech Science Press
date: 2020
type: Article
type: PeerReviewed
identifier:   Qayyum, A. and Ahmad, I. and Iftikhar, M. and Mazher, M.  (2020) Object detection and fuzzy-based classification using UAV data.  Intelligent Automation and Soft Computing, 26 (4).  pp. 693-702.  ISSN 10798587     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094108778&doi=10.32604%2fiasc.2020.010103&partnerID=40&md5=03300cd86dd70afec88422b7d4dd4ea0
relation: 10.32604/iasc.2020.010103
identifier: 10.32604/iasc.2020.010103