Darmawan, Matthew Rio and Ipung, Heru Purnomo and Galinium, Maulahikmah (2018) Outdoor Experiment Of Multispectral Sensing Sensor For Urban Road Materials. Bachelor thesis, Swiss German University.

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Abstract

This research is an improvement from previous work on experiment classifying urban road materials that tested on laboratory scale where this experiment is more focusing on experiment of multispectral sensing sensor for urban road materials in outdoor environment. This research aims to classify five urban road materials that are aggregates, asphalts, concrete, clay, natural fibre including vegetation and water. There were 9 cameras in the multispectral sensing sensor. Seven camera attached with narrow band optical filter sets with the centre spectrum at 710nm, 730nm, 750nm, 800nm, 870nm, 905nm and 950nm that are attached. One camera attached with normalization band is 720nm using high pass optical filter. The one camera attached with UV/IR cut optical filter to be work as RGB camera. The images result that has been taken processed in MATLAB to get the imaging index results from the multispectral system. Using Naïve Bayes classifier in Weka to classify the urban road materials with vegetation and water. The first classification and testing that classifies five urban road materials with vegetation and water have accuracy results ranged from 0 % to 32% while the accuracy results that classifies five urban road materials without vegetation and water have better accuracy results ranged from 0 % to 55 %.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Multispectral; Urban Road Materials; Imaging Index; Image Processing
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7872 Sensor Networks
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Astuti Kusumaningrum
Date Deposited:	31 May 2020 15:19
Last Modified:	31 May 2020 15:19
URI:	http://repository.sgu.ac.id/id/eprint/752

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