UAV-based extraction of topograhic and as-built information by object-based image analysis technique

The advancement of airborne technology without pilot, unmanned aerial vehicle (UAV) systems utilises the minimal cost of function for future mapping purposes. The utilisation of UAV data from visible red, green, blue (RGB) bands is limited to the visualisation of orthophoto for planning and monitori...

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Main Author:	Sibaruddin, Hairie Ilkham
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Drone aircraft Image analysis
Online Access:	http://psasir.upm.edu.my/id/eprint/76072/1/FK%202018%20151%20-%20IR.pdf
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id	my-upm-ir.76072
record_format	uketd_dc
spelling	my-upm-ir.760722019-11-29T08:40:39Z UAV-based extraction of topograhic and as-built information by object-based image analysis technique 2018-06 Sibaruddin, Hairie Ilkham The advancement of airborne technology without pilot, unmanned aerial vehicle (UAV) systems utilises the minimal cost of function for future mapping purposes. The utilisation of UAV data from visible red, green, blue (RGB) bands is limited to the visualisation of orthophoto for planning and monitoring applications. Thus, this study explores the potentials of UAV data based on RGB camera for topographic and as-built information for features extraction using OBIA (objectbased image analysis) technique. The main objective of this study is to assess the capability of UAV data in providing reliable topographic and as-built data information. More specifically, this study aims to extract topographic and as-built information such as land cover and geometry of infrastructure classes in urban area using eBee Sensefly UAV imagery. In this frame of study, The National Land and Survey Institute in Tanjung Malim, Perak, Malaysia was chosen as the area of interest. A robust Taguchi method was used in optimising the segmentation process. In accordance with the image classification process, different supervised OBIA classifiers such as KNN, normal Bayes (NB), decision tree (DT), random forest (RF), and support vector machine (SVM) were tested by tuning each of their parameters to quantify the performance of each classifier in favour of using UAV image data. Results showed that SVM obtained the highest percentage of overall accuracy, followed by RF, NB, DT, and KNN at 97.20%, 95.80%, 93.14%, 86.01% and 77.62%, respectively. The optimal OBIA parameters for each classifier are as follows: SVM with their C was 100,000 and Gamma 0.001. Meanwhile, the maximum depth parameter for RF and DT was 15 and 20 respectively. KNN classifier with K parameter was 5. The dimensional assessment on features that was extracted using OBIA showed that the error was in the range of less than 20 cm. Meanwhile, the positional accuracy based on root mean square error (RMSE) result gave the error of horizontal and vertical axes of less than 0.5 m. This result indicated that the UAV image data have a big potential to be utilised for topographic mapping and as-built information. Moreover, the OBIA technique also contributes to efficient features extraction compared to the manually practiced technique. Drone aircraft Image analysis 2018-06 Thesis http://psasir.upm.edu.my/id/eprint/76072/ http://psasir.upm.edu.my/id/eprint/76072/1/FK%202018%20151%20-%20IR.pdf text en public masters Universiti Putra Malaysia Drone aircraft Image analysis
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Drone aircraft Image analysis
spellingShingle	Drone aircraft Image analysis Sibaruddin, Hairie Ilkham UAV-based extraction of topograhic and as-built information by object-based image analysis technique
description	The advancement of airborne technology without pilot, unmanned aerial vehicle (UAV) systems utilises the minimal cost of function for future mapping purposes. The utilisation of UAV data from visible red, green, blue (RGB) bands is limited to the visualisation of orthophoto for planning and monitoring applications. Thus, this study explores the potentials of UAV data based on RGB camera for topographic and as-built information for features extraction using OBIA (objectbased image analysis) technique. The main objective of this study is to assess the capability of UAV data in providing reliable topographic and as-built data information. More specifically, this study aims to extract topographic and as-built information such as land cover and geometry of infrastructure classes in urban area using eBee Sensefly UAV imagery. In this frame of study, The National Land and Survey Institute in Tanjung Malim, Perak, Malaysia was chosen as the area of interest. A robust Taguchi method was used in optimising the segmentation process. In accordance with the image classification process, different supervised OBIA classifiers such as KNN, normal Bayes (NB), decision tree (DT), random forest (RF), and support vector machine (SVM) were tested by tuning each of their parameters to quantify the performance of each classifier in favour of using UAV image data. Results showed that SVM obtained the highest percentage of overall accuracy, followed by RF, NB, DT, and KNN at 97.20%, 95.80%, 93.14%, 86.01% and 77.62%, respectively. The optimal OBIA parameters for each classifier are as follows: SVM with their C was 100,000 and Gamma 0.001. Meanwhile, the maximum depth parameter for RF and DT was 15 and 20 respectively. KNN classifier with K parameter was 5. The dimensional assessment on features that was extracted using OBIA showed that the error was in the range of less than 20 cm. Meanwhile, the positional accuracy based on root mean square error (RMSE) result gave the error of horizontal and vertical axes of less than 0.5 m. This result indicated that the UAV image data have a big potential to be utilised for topographic mapping and as-built information. Moreover, the OBIA technique also contributes to efficient features extraction compared to the manually practiced technique.
format	Thesis
qualification_level	Master's degree
author	Sibaruddin, Hairie Ilkham
author_facet	Sibaruddin, Hairie Ilkham
author_sort	Sibaruddin, Hairie Ilkham
title	UAV-based extraction of topograhic and as-built information by object-based image analysis technique
title_short	UAV-based extraction of topograhic and as-built information by object-based image analysis technique
title_full	UAV-based extraction of topograhic and as-built information by object-based image analysis technique
title_fullStr	UAV-based extraction of topograhic and as-built information by object-based image analysis technique
title_full_unstemmed	UAV-based extraction of topograhic and as-built information by object-based image analysis technique
title_sort	uav-based extraction of topograhic and as-built information by object-based image analysis technique
granting_institution	Universiti Putra Malaysia
publishDate	2018
url	http://psasir.upm.edu.my/id/eprint/76072/1/FK%202018%20151%20-%20IR.pdf
_version_	1747813112509104128

UAV-based extraction of topograhic and as-built information by object-based image analysis technique

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