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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
Volume 9, Issue 1, June 2021, Pages: 1-15
Received: Dec. 14, 2020; Accepted: Dec. 25, 2020; Published: Jan. 4, 2021
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Authors
Alfred Homere Ngandam Mfondoum, StatsN'Maps, Private Consulting Firm, Dallas, USA; Laboratory of Natural Resources Management, Department of Geography, University of Yaounde I, Yaounde, Cameroon
Paul Gerard Gbetkom, Department of Geography, University of Aix-Marseille, Marseille, France
Sofia Hakdaoui, Earth Observation Department, Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Centre, Scientific Institute, Mohamed V University, 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; I Love Geomatics Association, Yaoundé, Cameroon
Brian Njumeneh, I Love Geomatics Association, Yaoundé, Cameroon
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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, 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.
Keywords
Brightness-shadowing, Built-up, Multi-Layers Perceptron, Linear Regression, Noise, Unplanned Cities
To cite this article
Alfred Homere Ngandam Mfondoum, Paul Gerard Gbetkom, Sofia Hakdaoui, Ryan Cooper, Armel Fabrice Mvogo Moto, Brian Njumeneh, 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. Vol. 9, No. 1, 2021, pp. 1-15. doi: 10.11648/j.ajrs.20210901.11
Copyright
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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