Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique

Lin, Zixun; Huang, Shiuh‐Jer; Lin, Chinsu

doi:10.1109/igarss47720.2021.9553715

Cited by 2 publications

(1 citation statement)

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“…In [12], segmentation and classification of orthophotos from UAVs using the method of reference vectors is proposed. The essence of this approach is in the combination of UAV orthophotography and machine learning models with a support vector machine (SVM).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Methods of UAVs images segmentation based on k-means and a genetic algorithm

Рубан

Khudov

Makoveichuk

et al. 2022

EEJET

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The object of this study is the process of segmentation of images from unmanned aerial vehicles. It was established that segmentation methods based on k-means and a genetic algorithm work qualitatively on images from space observation systems. It is proposed to use segmentation methods based on k-means and a genetic algorithm for segmenting images from unmanned aerial vehicles. The main stages of image segmentation methods based on k-means and genetic algorithm have been determined. An experimental study of segmentation of images from unmanned aerial vehicles was carried out. Unlike known ones, image segmentation by a k-means-based method that successfully works on images from space surveillance systems cannot be directly applied to image segmentation from unmanned aerial vehicles. Unlike known ones, image segmentation by a method based on a genetic algorithm that successfully works on images from space surveillance systems also cannot be directly applied to image segmentation from unmanned aerial vehicles. The quality of segmentation of images from unmanned aerial vehicles by methods based on k-means and a genetic algorithm was assessed. It was established that: – the average level of first-kind errors is 70 % and 51 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively; – average level of second-kind errors is 61 % and 43 % when segmenting an image from an unmanned aerial vehicle using methods based on k-means and a genetic algorithm, respectively. It was concluded that further research must be carried out to develop methods for segmenting images from unmanned aerial vehicles.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Methods of UAVs images segmentation based on k-means and a genetic algorithm

Рубан

Khudov

Makoveichuk

et al. 2022

EEJET

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A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique

Cited by 2 publications

References 9 publications

Methods of UAVs images segmentation based on k-means and a genetic algorithm

Methods of UAVs images segmentation based on k-means and a genetic algorithm

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

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