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contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)

Titre :	Automatic detection of clouds and shadows using high resolution satellite image time series
Type de document :	Article/Communication
Auteurs :	Nicolas Champion , Auteur
Editeur :	International Society for Photogrammetry and Remote Sensing ISPRS
Année de publication :	2016
Collection :	International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3
Projets :	1-Pas de projet /
Conférence :	ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3
Importance :	pp 475 - 479
Format :	21 x 30 cm
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'ombre [Termes IGN] détection des nuages [Termes IGN] image Landsat-8 [Termes IGN] image Pléiades-HR [Termes IGN] orthoimage [Termes IGN] réflectance de surface [Termes IGN] séquence d'images [Termes IGN] série temporelle
Résumé :	(auteur) Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences.
Numéro de notice :	C2016-038
Affiliation des auteurs :	IGN (2012-2019)
Thématique :	IMAGERIE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/isprs-archives-XLI-B3-475-2016
Date de publication en ligne :	09/06/2016
En ligne :	http://dx.doi.org/10.5194/isprs-archives-XLI-B3-475-2016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=91849

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contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)

Titre :	Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery
Type de document :	Article/Communication
Auteurs :	Clément Dechesne , Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; Valérie Gouet-Brunet , Auteur ; Alexandre Hervieu , Auteur
Editeur :	International Society for Photogrammetry and Remote Sensing ISPRS
Année de publication :	2016
Collection :	International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3
Projets :	1-Pas de projet /
Conférence :	ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3
Importance :	pp 207 - 214
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme Graph-Cut [Termes IGN] classification dirigée [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image à très haute résolution [Termes IGN] image multibande [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] semis de points
Résumé :	(auteur) Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).
Numéro de notice :	C2016-040
Affiliation des auteurs :	LASTIG MATIS (2012-2019)
Thématique :	FORET/IMAGERIE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/isprs-archives-XLI-B3-207-2016
Date de publication en ligne :	09/06/2016
En ligne :	https://doi.org/10.5194/isprs-archives-XLI-B3-207-2016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=91852

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contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)

Titre :	Evaluation of SIFT and SURF for vision based localization
Type de document :	Article/Communication
Auteurs :	Xiaozhi Qu , Auteur ; Bahman Soheilian , Auteur ; Emmanuel Habets , Auteur ; Nicolas Paparoditis , Auteur
Editeur :	International Society for Photogrammetry and Remote Sensing ISPRS
Année de publication :	2016
Collection :	International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3
Conférence :	ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3
Importance :	pp 685 - 692
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image [Termes IGN] compensation locale par faisceaux [Termes IGN] extraction de traits caractéristiques [Termes IGN] localisation basée vision [Termes IGN] point d'intérêt [Termes IGN] SIFT (algorithme) [Termes IGN] SURF (algorithme)
Résumé :	(auteur) Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.
Numéro de notice :	C2016-039
Affiliation des auteurs :	LASTIG MATIS (2012-2019)
Thématique :	IMAGERIE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/isprs-archives-XLI-B3-685-2016
Date de publication en ligne :	10/06/2016
En ligne :	https://doi.org/10.5194/isprs-archives-XLI-B3-685-2016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=91851

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contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)

Titre :	Uncertainty propagation for terrestrial mobile laser scanner
Type de document :	Article/Communication
Auteurs :	Miloud Mezian , Auteur ; Bruno Vallet , Auteur ; Bahman Soheilian , Auteur ; Nicolas Paparoditis , Auteur
Editeur :	International Society for Photogrammetry and Remote Sensing ISPRS
Année de publication :	2016
Collection :	International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3
Projets :	1-Pas de projet /
Conférence :	ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3
Importance :	pp 331 - 335
Format :	21 x 30 cm
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] géoréférencement direct [Termes IGN] matrice de covariance [Termes IGN] propagation d'incertitude [Termes IGN] semis de points
Résumé :	(auteur) Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.
Numéro de notice :	C2016-033
Affiliation des auteurs :	LASTIG MATIS (2012-2019)
Autre URL associée :	vers HAL
Thématique :	IMAGERIE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/isprs-archives-XLI-B3-331-2016
Date de publication en ligne :	09/06/2016
En ligne :	http://dx.doi.org/10.5194/isprs-archives-XLI-B3-331-2016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=91781

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contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)

Titre :	The iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds
Type de document :	Article/Communication
Auteurs :	Jan Böhm , Auteur ; Mathieu Brédif , Auteur ; T. Gierlinger, Auteur ; M. Krämer, Auteur ; R.E. Lindenberg, Auteur ; K. Liu, Auteur ; F. Michel, Auteur ; B. Sirmacek, Auteur
Editeur :	International Society for Photogrammetry and Remote Sensing ISPRS
Année de publication :	2016
Collection :	International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3
Projets :	IQmulus / Métral, Claudine
Conférence :	ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3
Importance :	pp 301 - 307
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] arbre urbain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] données massives [Termes IGN] semis de points [Termes IGN] Spark [Termes IGN] Toulouse [Termes IGN] traitement de données localisées
Résumé :	(auteur) Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study, the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.
Numéro de notice :	C2016-041
Affiliation des auteurs :	LASTIG MATIS+Ext (2012-2019)
Thématique :	IMAGERIE
Nature :	Communication
nature-HAL :	ComAvecCL&ActesPubliésIntl
DOI :	10.5194/isprs-archives-XLI-B3-301-2016
Date de publication en ligne :	09/06/2016
En ligne :	https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=91853

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The iQmulus urban showcase ... - pdf éditeur

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2014 -2024 IGN

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