Optical Remote Sensing in Urban Environments

Briottet, Xavier; Chehata, Nesrine; Oltra-Carrió, Rosa; Bris, Arnaud Le; Weber, Christiane

doi:10.1016/b978-1-78548-160-4.50001-7

Cited by 4 publications

(2 citation statements)

References 86 publications

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“…The land use map of the Oued Laou watershed is required in order to be used for the inputs to the model. The land use map is based on the supervised classification method [Briottet et al 2016], and is divided into seven classes: cultivated land, dam, bare soil, forests, rural and urban areas, pasture (Fig. 4).…”

Section: Data Collectionmentioning

confidence: 99%

Modeling of Continuous and Extreme Hydrological Processes Using Spatially Distributed Models MERCEDES, VICAIR and VISHYR in a Mediterranean Watershed

Aqnouy¹,

Messari²,

Bouadila³

et al. 2021

Ecol. Eng. Environ. Technol.

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Section: Data Collectionmentioning

confidence: 99%

Modeling of Continuous and Extreme Hydrological Processes Using Spatially Distributed Models MERCEDES, VICAIR and VISHYR in a Mediterranean Watershed

Aqnouy¹,

Messari²,

Bouadila³

et al. 2021

Ecol. Eng. Environ. Technol.

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“…This need can be efficiently fulfilled by the Earth observation technologies such as spatial remote sensors, able to gather quickly and recurrently a large quantity of image data, which are usable as part of many applications: air quality control, ground cartography, material aging monitoring, and vegetal biodiversity characterization. Indeed, optical remote sensing has proved to be a powerful tool in order to conduct urban studies (Briottet et al 2016). Multispectral high spatial resolution sensors are very efficient for the detection of urban objects and for the characterization of their size and shape.…”

Section: Introductionmentioning

confidence: 99%

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Roussel

Weber

Briottet

et al. 2017

International Journal of Remote Sensing

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For remote-sensing applications such as spectra classification or identification, atmospheric correction constitutes a very important pre-processing step, especially in complex urban environments where a lot of phenomenons alter the shape of the signal. The objective of this article is to compare the efficiency of two atmo-spheric correction algorithms, COCHISE (atmospheric COrrection Code for Hyperspectral Images of remote-sensing SEnsors) and an empirical method, on hyperspectral data and for classification applications. Classification is carried out on several simulated spaceborne data sets with different spatial resolutions (from 1.6 to 9.6 m). Four classifiers are considered in the study: a k-means, a Support Vector Machine (SVM), and a sun/ shadow version of each of them, which processes sunlit and shadowed pixels separately. Results show that the most relevant atmospheric method for classification depends on the spatial resolution of the processed data set. Indeed, if the empirical method performs better on high-resolution data sets (up to 4%), its superiority fades out as the spatial resolution decreases, especially with the lower spatial reso-lution where COCHISE can be 10% more accurate than the empiri-cal method.

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Spatio-temporal pattern of urban vegetation in the central business district of the Wa municipality of Ghana

Aabeyir

Peprah

Hackman³

2022

Trees, Forests and People

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Optical Remote Sensing in Urban Environments

Cited by 4 publications

References 86 publications

Modeling of Continuous and Extreme Hydrological Processes Using Spatially Distributed Models MERCEDES, VICAIR and VISHYR in a Mediterranean Watershed

Modeling of Continuous and Extreme Hydrological Processes Using Spatially Distributed Models MERCEDES, VICAIR and VISHYR in a Mediterranean Watershed

Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution

Spatio-temporal pattern of urban vegetation in the central business district of the Wa municipality of Ghana

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