Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds. The snow maps are currently produced and freely distributed on average 5&thinsp;d after the image acquisition as raster and vector files via the Theia portal (https://doi.org/10.24400/329360/F7Q52MNK).</p

Learning fuzzy rules to characterize objects of interest from remote sensing images

In this article a new method for learning concepts from examples of objects provided by experts for remote sensing images is presented. The goal of this method is to give the geographer expert a description of complex objects of interest extracted from very high resolution remote sensing images. The description of such objects needs to handle imprecision inherent to segmentation and very high resolution images. The first step of this approach is to classify objects composing all the examples. This classification allows the learning of a rule describing how the examples are composed regarding the segmentation. Finally, this rule is used to extract objects corresponding to the examples. Experiments on a remote sensing image of a urban landscape in Toulouse, France are presented to show the relevance of the method

Theia Snow collection: High-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

The Theia Snow collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers selected areas worldwide, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of the Theia Snow collection contains four classes: snow, no snow, cloud and no data. We present the algorithm to generate the snow products and provide an evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth measurements, higher-resolution snow maps and visual control. The results suggest that the snow is accurately detected in the Theia snow collection and that the snow detection is more accurate than the Sen2Cor outputs (ESA level 2 product). An issue that should be addressed in a future release is the occurrence of false snow detection in some large clouds

Learning fuzzy rules to characterize objects of interest from remote sensing images

International audienceIn this article a new method for learning concepts from examples of objects provided by experts for remote sensing images is presented. The goal of this method is to give the geographer expert a description of complex objects of interest extracted from very high resolution remote sensing images. The description of such objects needs to handle imprecision inherent to segmentation and very high resolution images. The first step of this approach is to classify objects composing all the examples. This classification allows the learning of a rule describing how the examples are composed regarding the segmentation. Finally, this rule is used to extract objects corresponding to the examples. Experiments on a remote sensing image of a urban landscape in Toulouse, France are presented to show the relevance of the method

Orfeo ToolBox : un outil open source pour traiter des images satellitaires

International audiencePrésentation d'une librairie open source pour traiter des images satellitaire

ORFEO, THE PLEIADES ACCOMPANIMENT PROGRAM AND ITS USERS THEMATIC COMMISSIONING

ORFEO, the PLEIADES Accompaniment Program, was set up by CNES, the French Space Agency, to prepare, accompany and promote the use and the exploitation of the images acquired by this Very High Resolution optical sensor. It was initiated in 2004 and will last until the end of the first year of the satellite life (launched in December 2011) . The Thematic part of the ORFEO accompaniment program covers a large range of applications, and aims at specifying and validating products and services required by users. An in-depth work of user needs assessments in eight thematic domains (sea and coastline, risks and humanitarian aid, cartography and urban planning, geophysical hazards, hydrology, forestry, agriculture and defence) has given rise to a large number of feasibility studies from 2006 to 2011. The Methodological Part of the ORFEO accompaniment program aims at preparing the use and exploitation of these submetric images. CNES decided to develop Orfeo Toolbox (OTB), an open source library capitalising the methodological know-how as a set of image processing and algorithmic components. Among other, OTB provides a number of heavily documented image processing functionalities such as filtering, feature extraction, segmentation, classification, change detection, 3D extraction, GIS links,.... As a conclusion to the ORFEO program, the PLEIADES Users Thematic Commissioning (UTC) started three months after the satellite launch and will last until mid 2013. It covers a large number of specific interest ORFEO sites, on which PLEIADES images are being intensively acquired and processed. These ORFEO sites have been chosen according to the expectations expressed by the users in terms of their interest for dedicated thematic, their geographic location and their multi-thematic content. This paper presents the ORFEO program achievements (thematic and methodology) and the organisation of the Users Thematic Commissioning (sites, studies). The paper is illustrated with some examples of multi-thematic studies, lead through ORFEO, covering a large range of applications, and aiming at validating value added products and services provided to end users from PLEIADES imagery

Utilisation des applications de l'Orfeo ToolBox

National audienceThis chapter presents the features offered by the Orfeo ToolBox (OTB) for spatial information extraction from remote sensing images. It describes the general presentation of the OTB namely: history, content, access and internal mechanisms, but also how to get it working, and what the main interfaces are when using it. The chapter examines the problems that are frequently encountered in remote sensing image processing: what applications can address a given problem, and how to use them. The OTB applications aim to provide users with a number of implemented processes. The chapter also presents the use of OTB applications typically encountered in remote sensing, from image preprocessing to retrieval of information. It introduces some preprocessing tools needed to produce data that can be used in a spatial context from raw remote sensing images. The chapter also describes some OTB applications for extracting information from the preprocessed images.Utilisation des applications de l'Orféo Toolbox pour traiter les images satellitaires