Titre :

Coastal and inland water pixels extraction algorithm (WiPE) from spectral shape analysis and HSV transformation applied to Landsat 8 OLI and Sentinel-2 MSI

Auteur(s) :

Ngoc, Dat Dinh [Auteur]
Loisel, Hubert [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Université du Littoral Côte d'Opale [ULCO]
Jamet, Cédric [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Université du Littoral Côte d'Opale [ULCO]
Vantrepotte, Vincent [Auteur]
Duforêt-Gaurier, Lucile [Auteur]
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 [LOG]
Université du Littoral Côte d'Opale [ULCO]
Minh, Chung Doan [Auteur]
Mangin, Antoine [Auteur]

Titre de la revue :

Remote Sensing of Environment

Pagination :

208-228

Éditeur :

Elsevier

Date de publication :

2019-03

ISSN :

0034-4257

Discipline(s) HAL :

Planète et Univers [physics]/Océan, Atmosphère

Résumé en anglais : [en]

Identification of water pixels over natural water bodies is a prerequisite step prior to applying algorithms dedicated to the estimation of bio-optical properties of surface waters from remote sensing observations. For ...
Lire la suite >Identification of water pixels over natural water bodies is a prerequisite step prior to applying algorithms dedicated to the estimation of bio-optical properties of surface waters from remote sensing observations. For visible remote sensing sensors, clouds affect the quantity and quality of the observations, directly by hiding part of the scene and indirectly by their shadows. A certain level of confusion could occur for detection of clouds over turbid (i.e. bright) waters and for detection of their shadows over any kind of surface water. Some algorithms exist but their performance is not satisfactory, especially over turbid waters where cloud-free pixels are sometimes classified as cloud or land, leading to a loss of data. This is particularly important for medium spatial resolution observations such as those performed by the Operational Land Imager (OLI) sensor on Landsat-8 or the Multispectral Instrument (MSI) on Sentinel-2 (a and b). In the frame of this study, we developed a two-step algorithm for the extraction of water pixels (referred to as WiPE) for these medium spatial resolution sensors. In contrast to other approaches based on the top of atmosphere (TOA) reflectance, this algorithm uses the Rayleigh-corrected TOA reflectance (ρrc(λ)) as input parameter allowing the spectral signature of each object to be better characterized. The first step, based on the ρrc(λ) spectral shape analysis of each object, allows water pixels to be discriminated from cloud, vegetation, barren land, and constructions pixels. The second step, in which the ρrc(λ) spectra are transferred into the Hue-Saturation-Value space, greatly improves the detection of cloud shadow over waters. This second step, based on the processing of the whole image, does not require any knowledge on the location and altitude of clouds. Thin clouds are identified during the two steps of the algorithm. This algorithm has been successfully tested over a broad range of environments. WiPE, specifically designed for the extraction of water pixels, generally shows better performance over turbid waters than the standard algorithm developed for Landsat imagery (Fmask). This is explained by the fact that Fmask does not specifically focus on the detection of water pixels, but on the masking of cloud, cloud shadow, and snow over land and waterLire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

• Laboratoire d'Océanologie et de Géosciences (LOG) - UMR 8187

Source :

Harvested from HAL