Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun

The land cover relate with physical feature of land surface. Land cover can be categories such as development area, vegetation areas, rural area, urban area and anything rely on the land surface. Remote sensing have been used to detect the changes of the land covers occurs by human activity. In t...

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Main Author:	Mohammad Harun, Muhammad Firdaus
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Remote Sensing Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/22690/1/TD_MUHAMMAD%20FIRDAUS%20MOHAMMAD%20HARUN%20AP%20R%2019.5.PDF
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id	my-uitm-ir.22690
record_format	uketd_dc
spelling	my-uitm-ir.226902019-01-11T01:49:39Z Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun 2019-01-09 Mohammad Harun, Muhammad Firdaus Remote Sensing Map drawing, modeling, printing, reading, etc Algorithms The land cover relate with physical feature of land surface. Land cover can be categories such as development area, vegetation areas, rural area, urban area and anything rely on the land surface. Remote sensing have been used to detect the changes of the land covers occurs by human activity. In this project, the objective is to generate supervised classification SPOT 7, to determine the accuracy of classification using maximum likelihood, minimum distance, mahalanobis distance and spectral angle algorithm and to produce the land use map. The algorithm were used to perform the supervised classification. The landuse were classified into six classes i.e. shrub, forest, paddy, cropland, build up and water. The accuracy assessment using error matrix method were done. A total of sixty (60) ground data were used to validate the accuracy of the classification. The result shows that maximum likelihood algorithm has the highest value for overall accuracy and overall kappa statistic which is 87% and 84% respectively. The lowest value shows by minimum distance algorithm is 68% and 61% respectively. 2019-01 Thesis https://ir.uitm.edu.my/id/eprint/22690/ https://ir.uitm.edu.my/id/eprint/22690/1/TD_MUHAMMAD%20FIRDAUS%20MOHAMMAD%20HARUN%20AP%20R%2019.5.PDF other en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Remote Sensing Remote Sensing Algorithms
spellingShingle	Remote Sensing Remote Sensing Algorithms Mohammad Harun, Muhammad Firdaus Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
description	The land cover relate with physical feature of land surface. Land cover can be categories such as development area, vegetation areas, rural area, urban area and anything rely on the land surface. Remote sensing have been used to detect the changes of the land covers occurs by human activity. In this project, the objective is to generate supervised classification SPOT 7, to determine the accuracy of classification using maximum likelihood, minimum distance, mahalanobis distance and spectral angle algorithm and to produce the land use map. The algorithm were used to perform the supervised classification. The landuse were classified into six classes i.e. shrub, forest, paddy, cropland, build up and water. The accuracy assessment using error matrix method were done. A total of sixty (60) ground data were used to validate the accuracy of the classification. The result shows that maximum likelihood algorithm has the highest value for overall accuracy and overall kappa statistic which is 87% and 84% respectively. The lowest value shows by minimum distance algorithm is 68% and 61% respectively.
format	Thesis
qualification_level	Bachelor degree
author	Mohammad Harun, Muhammad Firdaus
author_facet	Mohammad Harun, Muhammad Firdaus
author_sort	Mohammad Harun, Muhammad Firdaus
title	Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
title_short	Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
title_full	Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
title_fullStr	Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
title_full_unstemmed	Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun
title_sort	comparison of supervised classification technique of landuse map using high resolution image / muhammad firdaus mohammad harun
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/22690/1/TD_MUHAMMAD%20FIRDAUS%20MOHAMMAD%20HARUN%20AP%20R%2019.5.PDF
_version_	1783733824082935808

Comparison of supervised classification technique of landuse map using high resolution image / Muhammad Firdaus Mohammad Harun

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