@Article{iasc.2020.010103,
AUTHOR = {Abdul Qayyum, Iftikhar Ahmad, Mohsin Iftikhar, Moona Mazher},
TITLE = {Object Detection and Fuzzy-Based Classification Using UAV Data},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {693--702},
URL = {http://www.techscience.com/iasc/v26n4/40273},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2020.010103}
}