Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim

Urban area refers to cities which have highly heterogeneous objects with complex landscape. Variation land-cover features, which include natural and man-made objects, lead to the advent of features that are spectrally very similar. For example, trees and grass as well as building roofs and roads oft...

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Main Author:	Hashim, Nurhanisah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Malaysia
Online Access:	http://ir.uitm.edu.my/id/eprint/26838/ http://ir.uitm.edu.my/id/eprint/26838/1/TM_NURHANISAH%20HASHIM%20AP%2018_5.pdf

id	uitm-26838
recordtype	eprints
spelling	uitm-268382019-12-10T07:55:14Z http://ir.uitm.edu.my/id/eprint/26838/ Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim Hashim, Nurhanisah Malaysia Urban area refers to cities which have highly heterogeneous objects with complex landscape. Variation land-cover features, which include natural and man-made objects, lead to the advent of features that are spectrally very similar. For example, trees and grass as well as building roofs and roads often have similar spectral response that always had been misclassified yet difficult to distinguish. The complexness of urban features require high spatial as well as high spectral resolution image especially when trying to extract the land-cover objects. Advanced in remote sensing technology has tremendously strengthens the spatial and spectral techniques, which will greatly affects urban environment study. The emerging of high spectral resolution image with submeter level of accuracy lead to high potential in order to identify detailed urban landcover features. By adopting object based approached instead of pixel based will avoid the 'salt and pepper' effect that will decrease the accuracy of land-cover classification. Using Worldview-2 multispectral satellite image as a primary data, together with ancillary data which include normalized Digital Surface Model (nDSM) derived from Light Detection and Ranging (LIDAR) data and indices layer, the image segmentation process utilizing multiresolution segmentation algorithm was conducted. Twelve segmentation levels were constructed in order to create meaningful image objects before going through the classification process. Three supervised object based classifier namely Support Vector Machine (SVM), BAYES and K-Nearest Neighbour (KNN) were tested in order to identify which classifier gives the best classification result of the urban area of the study. Thirteen sets of experiment were created, which consist of different combination bands (8 multispectral band of worldview-2, panchromatic band, indices layer, texture image, spectral transformation image) to be tested by each classifier. The results from the study indicate statistically significant difference in classification accuracy between each classifier and experiment sets: SVM outperforms BAYES and KNN as produced highest overall accuracy (87.93%), however based on Kappa statistics per class, user's and producer's accuracy, as well as visual examination and overall accuracy performance, BAYES with overall accuracy of 85.51% has depicted to have the best land-cover classification accuracy result. 2018 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/26838/1/TM_NURHANISAH%20HASHIM%20AP%2018_5.pdf Hashim, Nurhanisah (2018) Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim. Masters thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Malaysia
spellingShingle	Malaysia Hashim, Nurhanisah Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
description	Urban area refers to cities which have highly heterogeneous objects with complex landscape. Variation land-cover features, which include natural and man-made objects, lead to the advent of features that are spectrally very similar. For example, trees and grass as well as building roofs and roads often have similar spectral response that always had been misclassified yet difficult to distinguish. The complexness of urban features require high spatial as well as high spectral resolution image especially when trying to extract the land-cover objects. Advanced in remote sensing technology has tremendously strengthens the spatial and spectral techniques, which will greatly affects urban environment study. The emerging of high spectral resolution image with submeter level of accuracy lead to high potential in order to identify detailed urban landcover features. By adopting object based approached instead of pixel based will avoid the 'salt and pepper' effect that will decrease the accuracy of land-cover classification. Using Worldview-2 multispectral satellite image as a primary data, together with ancillary data which include normalized Digital Surface Model (nDSM) derived from Light Detection and Ranging (LIDAR) data and indices layer, the image segmentation process utilizing multiresolution segmentation algorithm was conducted. Twelve segmentation levels were constructed in order to create meaningful image objects before going through the classification process. Three supervised object based classifier namely Support Vector Machine (SVM), BAYES and K-Nearest Neighbour (KNN) were tested in order to identify which classifier gives the best classification result of the urban area of the study. Thirteen sets of experiment were created, which consist of different combination bands (8 multispectral band of worldview-2, panchromatic band, indices layer, texture image, spectral transformation image) to be tested by each classifier. The results from the study indicate statistically significant difference in classification accuracy between each classifier and experiment sets: SVM outperforms BAYES and KNN as produced highest overall accuracy (87.93%), however based on Kappa statistics per class, user's and producer's accuracy, as well as visual examination and overall accuracy performance, BAYES with overall accuracy of 85.51% has depicted to have the best land-cover classification accuracy result.
format	Thesis
author	Hashim, Nurhanisah
author_facet	Hashim, Nurhanisah
author_sort	Hashim, Nurhanisah
title	Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
title_short	Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
title_full	Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
title_fullStr	Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
title_full_unstemmed	Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim
title_sort	urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / nurhanisah hashim
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/26838/ http://ir.uitm.edu.my/id/eprint/26838/1/TM_NURHANISAH%20HASHIM%20AP%2018_5.pdf
first_indexed	2023-09-18T23:17:29Z
last_indexed	2023-09-18T23:17:29Z
_version_	1777419208973352960

Urban landcover features identification utilizing multiband combinations and multi-level image segmentation for objectbased classification / Nurhanisah Hashim

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