Mapping of Built up Areas in the Cameroonians Shores of Lake Chad and its Hinterland through based Object Classification of Sentinel 2 Data

Gbetkom, Paul Gérard; Gadal, Sébastien; Aboudi, Ahmed El; Ng, Alfred Homère; Mfondoum, am

doi:10.5373/jardcs/v11sp11/20193133

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“…inside cities and along asphalted or dusty roads, as well as suburbanization that blurs the limits of modern built-up and craft made housing. It has been noticed that the pixel combinations in areas with heterogenic objects [13,22,23] causes high rate of uncertainties and biases [9,[23][24][25]. Moreover, spectral signatures of impervious surface areas are similar to those of dry soils [10,11], whereas detecting surface water is a challenge in regard of its confusion with buildings' shadowing [24].…”

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confidence: 99%

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

Mfondoum¹,

Gbetkom²,

Hakdaoui³

et al. 2021

AJRS

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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 r 2 =0.78.

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