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Object Detection and Fuzzy-Based Classification Using UAV Data

机译:使用UAV数据的对象检测和基于模糊的分类

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摘要

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.
机译:UAV(无人驾驶飞行器)配备有遥感设备的空间数据可以获得具有相关的相关区域的空间数据。在本文中,我们已经收购了高压电源杆,城市和植被/在电力线附近的植被/树木的UAV数据。对于对象分类,基于模糊分类器的所提出的方法与传统的最小距离分类器和我们三个定义的UAV图像段上的最大似然分类器进行比较。所有分类器的性能评估基于包括由UAV获取的图像中存在的图像中存在的每个对象的平均值,标准偏差和PDF(概率密度函数)的统计参数,并且计算了UAV图像的每个信道的差异。结果表明,与其他分类器相比,基于模糊的分类器优于。我们通过模糊基分类器实现了93%的分类准确性。

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