Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Alfred Homere Ngandam Mfondoum; Paul Gerard Gbetkom; Sofia Hakdaoui; Ryan Cooper; Armel Fabrice Mvogo Moto; Brian Njumeneh

doi:doi:10.11648/j.ajrs.20210901.11

Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise

Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, Brian Njumeneh

Published in American Journal of Remote Sensing (Volume 9, Issue 1)

Received: 14 December 2020 Accepted: 25 December 2020 Published: 4 January 2021

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Abstract

Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r²=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r²=0.75. The MLP r² standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r² standing for the noise is used to weigh the brightness temperature, T_B in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.

Published in	American Journal of Remote Sensing (Volume 9, Issue 1)
DOI	10.11648/j.ajrs.20210901.11
Page(s)	1-15
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), 2021. Published by Science Publishing Group

Keywords

Brightness-shadowing, Built-up, Multi-Layers Perceptron, Linear Regression, Noise, Unplanned Cities

References

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Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, et al. (2021). Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. American Journal of Remote Sensing, 9(1), 1-15. https://doi.org/10.11648/j.ajrs.20210901.11

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Alfred Homere Ngandam Mfondoum; Paul Gerard Gbetkom; Sofia Hakdaoui; Ryan Cooper; Armel Fabrice Mvogo Moto, et al. Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. Am. J. Remote Sens. 2021, 9(1), 1-15. doi: 10.11648/j.ajrs.20210901.11

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AMA Style

Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, et al. Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. Am J Remote Sens. 2021;9(1):1-15. doi: 10.11648/j.ajrs.20210901.11

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@article{10.11648/j.ajrs.20210901.11,
  author = {Alfred Homere Ngandam Mfondoum and Paul Gerard Gbetkom and Sofia Hakdaoui and Ryan Cooper and Armel Fabrice Mvogo Moto and Brian Njumeneh},
  title = {Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise},
  journal = {American Journal of Remote Sensing},
  volume = {9},
  number = {1},
  pages = {1-15},
  doi = {10.11648/j.ajrs.20210901.11},
  url = {https://doi.org/10.11648/j.ajrs.20210901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20210901.11},
  abstract = {Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.},
 year = {2021}
}

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TY  - JOUR
T1  - Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise
AU  - Alfred Homere Ngandam Mfondoum
AU  - Paul Gerard Gbetkom
AU  - Sofia Hakdaoui
AU  - Ryan Cooper
AU  - Armel Fabrice Mvogo Moto
AU  - Brian Njumeneh
Y1  - 2021/01/04
PY  - 2021
N1  - https://doi.org/10.11648/j.ajrs.20210901.11
DO  - 10.11648/j.ajrs.20210901.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 1
EP  - 15
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20210901.11
AB  - Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.
VL  - 9
IS  - 1
ER  -

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Author Information

Alfred Homere Ngandam Mfondoum

StatsN'Maps, Private Consulting Firm, Dallas, USA
Paul Gerard Gbetkom

Department of Geography, University of Aix-Marseille, Marseille, France
Sofia Hakdaoui

Geoscience, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Av. Ibn Batouta, Rabat, Morocco
Ryan Cooper

Erik Jonsson School of Engineering and Computer Science, University of Texas in Dallas, Richardson, Texas, USA
Armel Fabrice Mvogo Moto

Laboratory of Natural Resources Management, Department of Geography, University of Yaounde I, Yaounde, Cameroon
Brian Njumeneh

I Love Geomatics Association, Yaoundé, Cameroon

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Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, et al. (2021). Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. American Journal of Remote Sensing, 9(1), 1-15. https://doi.org/10.11648/j.ajrs.20210901.11

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Alfred Homere Ngandam Mfondoum; Paul Gerard Gbetkom; Sofia Hakdaoui; Ryan Cooper; Armel Fabrice Mvogo Moto, et al. Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. Am. J. Remote Sens. 2021, 9(1), 1-15. doi: 10.11648/j.ajrs.20210901.11

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AMA Style

Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, et al. Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise. Am J Remote Sens. 2021;9(1):1-15. doi: 10.11648/j.ajrs.20210901.11

Copy | Download

@article{10.11648/j.ajrs.20210901.11,
  author = {Alfred Homere Ngandam Mfondoum and Paul Gerard Gbetkom and Sofia Hakdaoui and Ryan Cooper and Armel Fabrice Mvogo Moto and Brian Njumeneh},
  title = {Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise},
  journal = {American Journal of Remote Sensing},
  volume = {9},
  number = {1},
  pages = {1-15},
  doi = {10.11648/j.ajrs.20210901.11},
  url = {https://doi.org/10.11648/j.ajrs.20210901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20210901.11},
  abstract = {Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.},
 year = {2021}
}

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TY  - JOUR
T1  - Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise
AU  - Alfred Homere Ngandam Mfondoum
AU  - Paul Gerard Gbetkom
AU  - Sofia Hakdaoui
AU  - Ryan Cooper
AU  - Armel Fabrice Mvogo Moto
AU  - Brian Njumeneh
Y1  - 2021/01/04
PY  - 2021
N1  - https://doi.org/10.11648/j.ajrs.20210901.11
DO  - 10.11648/j.ajrs.20210901.11
T2  - American Journal of Remote Sensing
JF  - American Journal of Remote Sensing
JO  - American Journal of Remote Sensing
SP  - 1
EP  - 15
PB  - Science Publishing Group
SN  - 2328-580X
UR  - https://doi.org/10.11648/j.ajrs.20210901.11
AB  - Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.
VL  - 9
IS  - 1
ER  -

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