A knowledge based classification for urban mapping using high resolution remote sensing data

Xiao Yi; Xiuping Jia; David Tien

doi:10.1109/DICTA.2007.4426851

A knowledge based classification for urban mapping using high resolution remote sensing data

Xiao Yi, Xiuping Jia, David Tien

University College

Research output: Book chapter/Published conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

This paper addresses the weakness of pixel based or cell-based classification algorithms for urban mapping. They cannot provide a handy object level classification results as often preferred in urban planning and assessment applications. A knowledge based approach is proposed which is integrated with the spectral classification methods and image processing tools. Experiments using a high resolution image were conducted. The results show the proposed method can improve the performances obtained by using the conventional maximum likelihood or ECHO algorithms.

Original language	English
Title of host publication	Proceedings - Digital Image Computing Techniques and Applications
Subtitle of host publication	9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007
Pages	586-591
Number of pages	6
DOIs	https://doi.org/10.1109/DICTA.2007.4426851
Publication status	Published - 01 Dec 2007
Event	Australian Pattern Recognition Society (APRS) - Glenelg, SA, Australia Duration: 03 Dec 2007 → 05 Dec 2007

Conference

Conference	Australian Pattern Recognition Society (APRS)
Country/Territory	Australia
City	Glenelg, SA
Period	03/12/07 → 05/12/07

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10.1109/DICTA.2007.4426851

Cite this

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title = "A knowledge based classification for urban mapping using high resolution remote sensing data",

abstract = "This paper addresses the weakness of pixel based or cell-based classification algorithms for urban mapping. They cannot provide a handy object level classification results as often preferred in urban planning and assessment applications. A knowledge based approach is proposed which is integrated with the spectral classification methods and image processing tools. Experiments using a high resolution image were conducted. The results show the proposed method can improve the performances obtained by using the conventional maximum likelihood or ECHO algorithms.",

keywords = "Classification, Knowledge based, Object attributes",

author = "Xiao Yi and Xiuping Jia and David Tien",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/DICTA.2007.4426851",

language = "English",

isbn = "0769530672",

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Yi, X, Jia, X & Tien, D 2007, A knowledge based classification for urban mapping using high resolution remote sensing data. in Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007., 4426851, pp. 586-591, Australian Pattern Recognition Society (APRS), Glenelg, SA, Australia, 03/12/07. https://doi.org/10.1109/DICTA.2007.4426851

A knowledge based classification for urban mapping using high resolution remote sensing data. / Yi, Xiao; Jia, Xiuping; Tien, David.
Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007. 2007. p. 586-591 4426851.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - A knowledge based classification for urban mapping using high resolution remote sensing data

AU - Yi, Xiao

AU - Jia, Xiuping

AU - Tien, David

PY - 2007/12/1

Y1 - 2007/12/1

N2 - This paper addresses the weakness of pixel based or cell-based classification algorithms for urban mapping. They cannot provide a handy object level classification results as often preferred in urban planning and assessment applications. A knowledge based approach is proposed which is integrated with the spectral classification methods and image processing tools. Experiments using a high resolution image were conducted. The results show the proposed method can improve the performances obtained by using the conventional maximum likelihood or ECHO algorithms.

AB - This paper addresses the weakness of pixel based or cell-based classification algorithms for urban mapping. They cannot provide a handy object level classification results as often preferred in urban planning and assessment applications. A knowledge based approach is proposed which is integrated with the spectral classification methods and image processing tools. Experiments using a high resolution image were conducted. The results show the proposed method can improve the performances obtained by using the conventional maximum likelihood or ECHO algorithms.

KW - Classification

KW - Knowledge based

KW - Object attributes

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BT - Proceedings - Digital Image Computing Techniques and Applications

T2 - Australian Pattern Recognition Society (APRS)

Y2 - 3 December 2007 through 5 December 2007

ER -

A knowledge based classification for urban mapping using high resolution remote sensing data

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