Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery

Selassie David Mayunga

doi:doi:10.11648/j.ijiis.20221101.13

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Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery

Selassie David Mayunga

Published in International Journal of Intelligent Information Systems (Volume 11, Issue 1)

Received: 22 February 2022 Accepted: 12 March 2022 Published: 18 March 2022

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Abstract

The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.

Published in	International Journal of Intelligent Information Systems (Volume 11, Issue 1)
DOI	10.11648/j.ijiis.20221101.13
Page(s)	7-13
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Radial Casting Algorithm, High-resolution Image, Building Extraction

References

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APA Style

Selassie David Mayunga. (2022). Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. International Journal of Intelligent Information Systems, 11(1), 7-13. https://doi.org/10.11648/j.ijiis.20221101.13

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ACS Style

Selassie David Mayunga. Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. Int. J. Intell. Inf. Syst. 2022, 11(1), 7-13. doi: 10.11648/j.ijiis.20221101.13

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AMA Style

Selassie David Mayunga. Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. Int J Intell Inf Syst. 2022;11(1):7-13. doi: 10.11648/j.ijiis.20221101.13

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@article{10.11648/j.ijiis.20221101.13,
  author = {Selassie David Mayunga},
  title = {Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {1},
  pages = {7-13},
  doi = {10.11648/j.ijiis.20221101.13},
  url = {https://doi.org/10.11648/j.ijiis.20221101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221101.13},
  abstract = {The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.},
 year = {2022}
}

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TY  - JOUR
T1  - Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery
AU  - Selassie David Mayunga
Y1  - 2022/03/18
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221101.13
DO  - 10.11648/j.ijiis.20221101.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 7
EP  - 13
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221101.13
AB  - The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.
VL  - 11
IS  - 1
ER  -

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Author Information

Selassie David Mayunga

Department of Civil and Environmental Engineering, Botswana International University of Science and Technology, Palapye, Republic of Botswana

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Plain Text BibTeX RIS

APA Style

Selassie David Mayunga. (2022). Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. International Journal of Intelligent Information Systems, 11(1), 7-13. https://doi.org/10.11648/j.ijiis.20221101.13

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ACS Style

Selassie David Mayunga. Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. Int. J. Intell. Inf. Syst. 2022, 11(1), 7-13. doi: 10.11648/j.ijiis.20221101.13

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AMA Style

Selassie David Mayunga. Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery. Int J Intell Inf Syst. 2022;11(1):7-13. doi: 10.11648/j.ijiis.20221101.13

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@article{10.11648/j.ijiis.20221101.13,
  author = {Selassie David Mayunga},
  title = {Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery},
  journal = {International Journal of Intelligent Information Systems},
  volume = {11},
  number = {1},
  pages = {7-13},
  doi = {10.11648/j.ijiis.20221101.13},
  url = {https://doi.org/10.11648/j.ijiis.20221101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20221101.13},
  abstract = {The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery
AU  - Selassie David Mayunga
Y1  - 2022/03/18
PY  - 2022
N1  - https://doi.org/10.11648/j.ijiis.20221101.13
DO  - 10.11648/j.ijiis.20221101.13
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 7
EP  - 13
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20221101.13
AB  - The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.
VL  - 11
IS  - 1
ER  -

Copy | Download