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Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)
[article]
Titre : Information fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification Type de document : Article/Communication Auteurs : S. Prasad, Auteur ; J. Fowler, Auteur ; L. Bruce, Auteur ; W. Li, Auteur Année de publication : 2012 Article en page(s) : pp 3474 - 3486 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] filtrage du bruit
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] partitionnement
[Termes IGN] redondance de données
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Hyperspectral imagery comprises high-dimensional reflectance vectors representing the spectral response over a wide range of wavelengths per pixel in the image. The resulting high-dimensional feature spaces often result in statistically ill-conditioned class-conditional distributions. Conventional methods for alleviating this problem typically employ dimensionality reduction such as linear discriminant analysis along with single-classifier systems, yet these methods are suboptimal and lack noise robustness. In contrast, a divide-and-conquer approach is proposed to address the high dimensionality of hyperspectral data for effective and noise-robust classification. Central to the proposed framework is a redundant wavelet transform for representing the data in a feature space amenable to noise-robust multiscale analysis as well as a multiclassifier and decision-fusion system for classification and target recognition in high-dimensional spaces under small-sample-size conditions. The proposed partitioning of this feature space assigns a collection of all coefficients across all scales at a particular spectral wavelength to a dedicated classifier. It is demonstrated that such a partitioning of the feature space for a multiclassifier system yields superior noise performance for classification tasks. Additionally, validation studies with experimental hyperspectral data show that the proposed system significantly outperforms conventional denoising and classification approaches. Numéro de notice : A2012-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185053 Date de publication en ligne : 06/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31897
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3474 - 3486[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2012091 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Improving 3D lidar point cloud registration using optimal neighborhood knowledge / Adrien Gressin in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol I-3 (2012)
[article]
Titre : Improving 3D lidar point cloud registration using optimal neighborhood knowledge Type de document : Article/Communication Auteurs : Adrien Gressin , Auteur ; Clément Mallet , Auteur ; Nicolas David , Auteur Année de publication : 2012 Projets : 1-Pas de projet / Conférence : ISPRS 2012, Commission 3, 22th international congress 25/08/2012 01/09/2012 Melbourne Australie OA ISPRS Annals Commission 3 Article en page(s) : pp 111 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] superposition de données
[Termes IGN] tenseur
[Termes IGN] valeur propre
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) Automatic 3D point cloud registration is a main issue in computer vision and photogrammetry. The most commonly adopted solution is the well-known ICP (Iterative Closest Point) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, and assuming that good a priori alignment is provided. A large body of literature has proposed many variations of this algorithm in order to improve each step of the process. The aim of this paper is to demonstrate how the knowledge of the optimal neighborhood of each 3D point can improve the speed and the accuracy of each of these steps. We will first present the geometrical features that are the basis of this work. These low-level attributes describe the shape of the neighborhood of each 3D point, computed by combining the eigenvalues of the local structure tensor. Furthermore, they allow to retrieve the optimal size for analyzing the neighborhood as well as the privileged local dimension (linear, planar, or volumetric). Besides, several variations of each step of the ICP process are proposed and analyzed by introducing these features. These variations are then compared on real datasets, as well with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Finally, the method is successfully applied to various 3D lidar point clouds both from airborne, terrestrial and mobile mapping systems. Numéro de notice : A2012-712 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-I-3-111-2012 En ligne : http://dx.doi.org/10.5194/isprsannals-I-3-111-2012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82701
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol I-3 (2012) . - pp 111 - 116[article]Trajectory-based registration of 3d lidar point clouds acquired with a mobile mapping system / Adrien Gressin in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol I-3 (2012)
[article]
Titre : Trajectory-based registration of 3d lidar point clouds acquired with a mobile mapping system Type de document : Article/Communication Auteurs : Adrien Gressin , Auteur ; Bertrand Cannelle , Auteur ; Clément Mallet , Auteur ; Jean-Pierre Papelard , Auteur Année de publication : 2012 Conférence : ISPRS 2012, Commission 3, 22th international congress 25/08/2012 01/09/2012 Melbourne Australie OA ISPRS Annals Commission 3 Article en page(s) : pp 117 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] système de numérisation mobile
[Termes IGN] traitement de données localisées
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) Thanks to a hybrid georeferencing unit coupling GNSS and IMU sensors, mobile mapping systems (MMS) with lidar sensors provide accurate 3D point clouds of the acquired areas, mainly urban cities. When dealing with several acquisitions of the same area with the same device, differences in the range of several tens of centimeters can be observed. Such degradation of the georeferencing accuracies are due to two main reasons: inertial drift and losses of GNSS signals in urban corridors. The purpose of this paper is therefore to correct these differences with an accurate ICP-based registration algorithm, and then to correct the MMS trajectory using these retrieved local transformation parameters.The trajectory loop information plays a key role for that purpose. We propose a four-step method starting from a 3D point cloud with overlapping parts, and the trajectory of the mobile mapping system. First, a polygonal approximation of the trajectory is computed in order to first divide the whole registration problem in local sub-issues. Secondly, we aim to find all the potential overlapping acquired areas between these segments using simple bounding box intersections. Thirdly, for each pair of overlapping areas, an efficient variant of the ICP algorithm is proposed to (1) prune cases where segments do not share point clouds of the same areas and (2) retrieve the transformation parameters, for real overlapping cases. Finally, all these transformations are linked together, and fed into a global distance compensation problem, allowing to adjust the MMS trajectories for several passages. As a conclusion, this method is successfully applied to data acquired over Paris (France) with the Stereopolis mobile mapping system. Numéro de notice : A2012-711 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-I-3-117-2012 Date de publication en ligne : 20/07/2012 En ligne : http://dx.doi.org/10.5194/isprsannals-I-3-117-2012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82700
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol I-3 (2012) . - pp 117 - 122[article]
Titre : Change detection in lidar scans of urban environments Type de document : Thèse/HDR Auteurs : Alexandri Gregor Zavodny, Auteur Editeur : South bend [Indiana - Etats-Unis] : University of Notre Dame Année de publication : 2012 Importance : 146 p. Note générale : bibliographie
A dissertation submitted to the Graduate School of the University of Notre Dame in partial fulfillment of the requirements for the degree of doctor of PhilosophyLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] jeu de données localisées
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de pointsRésumé : (auteur) Light Detection and Ranging (LIDAR) is a popular tool for range sensing applications, with applications including environmental modeling and urban planning. Modern acquisition platforms facilitate data collection rates of over two billion points per hour, enabling the collection of massive datasets with ease. These datasets must typically undergo a large amount of processing before use, making maintenance a difficult issue. Additionally, the presence of transient objects can affect dataset quality, both as unwanted data and by obscuring the underlying surface model. In this dissertation, we present a framework for enabling automatic change detection between large LIDAR datasets of urban environments. For two large scale datasets, we extract the relevant portions using information about the paths of the acquisition vehicles. However, acquisition inaccuracies can result in subset misalignment of up to 10 meters. To correct for this, we utilize a variety of point cloud alignment techniques, including a novel point descriptor, to bring overlapping pieces of data into alignment. Given the properly aligned data, we then use novel hierarchical and point-based techniques to extract regions of change between the two datasets. These regions can then be extracted and presented for further processing or filtering. We present the results of our research executed on datasets totaling over 93 billion sample points. At over 1.5 terabytes in size, this represents by far the largest collection of ground-based LIDAR examined in the open literature. We quantify our results with a variety of objective metrics, investigate modes of failure, and recommend directions for future research. Numéro de notice : 17369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Computer Science and Engineering : Notre Dame Indiana USA : 2012 DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84245 Documents numériques
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Change detection in lidar scansAdobe Acrobat PDF
Titre : Tree species classification with multiple source remote sensing data Type de document : Thèse/HDR Auteurs : Eetu Puttonen, Auteur ; Juha Hyyppä, Directeur de thèse Editeur : Helsinki : Finnish Geodetic Institute FGI Année de publication : 2012 Collection : Publications of the Finnish Geodetic Institute, ISSN 0085-6932 num. 145 Importance : 86 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-951-711-289-5 Note générale : Doctoral dissertation University of Helsinki, Faculty of Science, Department of Physics, geophysics and astronomy Finnish Geodetic Institute, Department of Photogrammetry and Remote Sensing
ISBN du pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse discriminante
[Termes IGN] arbre (flore)
[Termes IGN] capteur hyperspectral
[Termes IGN] classification
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] espèce végétale
[Termes IGN] fusion de données
[Termes IGN] image spectrale
[Termes IGN] semis de pointsRésumé : (auteur) Remote sensing is a study that provides information on targets of interest without direct interaction with them. Generally, the term is used for measurement techniques that detect electro-magnetic radiation emitted or reflected from the targets.
Commonly used wavelength ranges include visible, infra-red, microwaves, and thermal bands. This information can be exploited to determine the structural and spectral properties of targets. Remote sensing techniques are typically utilized in mapping solutions, environment monitoring, target recognition, change detection, and in creation of physical models.
In Finland, remote sensing research is of specific importance in forest sciences and industry as they need precise information on tree quantity and quality over large forest ranges. Tree species information on individual tree level is an important parameter to achieve this goal.
The aim of this thesis is to study how individual tree species information can be extracted with multiple source remote sensing data. The aim is achieved by combining spatial and spectral remote sensing data. Structural properties of individual trees are determined from three dimensional point clouds collected with laser scanners. Spectral properties of trees are collected with cameras or spectrometers.
The thesis consists of four separate studies. The first study examined how shading information of trees canopies could be exploited to improve tree species classification in data collected with airborne sensors. The second study examined the classification performance of a low-cost, multi-sensor, mobile mapping system. The third study investigated the classification performance and accuracy of a novel, active hyperspectral laser scanner. Finally, the fourth study evaluated the suitability of artificial surfaces as on-site intensity calibration targets.
The results of the three classification studies showed that the use of combined point cloud and spectral information yielded the best classification results in all study cases when compared against classification results obtained with only structural or spectral information. Moreover, the studies showed that the improved results could be achieved with a low total number of mixed structural and spectral classification parameters. The fourth study showed that the artificial surfaces work as calibration surfaces only in limited cases.
The main outcome of the thesis was that the active remote sensing systems measuring multiple wavelengths simultaneously should be promoted. They have a significant potential to improve tree species classification performance even with a few application-specific wavelengths.Numéro de notice : 15863 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : Doctoral dissertation : Photogrammetry and Remote Sensing : University of Helsinki : 2012 En ligne : http://hdl.handle.net/10138/33956 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93650 Automatic roof model reconstruction from ALS data and 2D ground plans based on side projection and the TMR [TIN-Merging and Reshaping] algorithm / J. Rau in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkData fusion of extremely high resolution aerial imagery and LiDAR data for automated railroad centre line reconstruction / R. Beger in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkFusion of camera images and laser scans for wide baseline 3D scene alignment in urban environments / Michael Ying Yang in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkA quality prediction method for building model reconstruction using LiDAR data and topographic maps / R. You in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)PermalinkElectromagnetic land surface classification through integration of optical and radar remote sensing data / J. Baek in IEEE Transactions on geoscience and remote sensing, vol 49 n° 4 (April 2011)PermalinkChange detection in a topographic building database using submetric satellite images / Arnaud Le Bris (2011)PermalinkPermalinkHigh resolution topographic model of Panarea Island by fusion of photogrammetric, lidar and bathymetric digital terrain models / Massimo Fabris in Photogrammetric record, vol 25 n° 132 (December 2010 - February 2011)PermalinkMulti-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments / Daniela Craciun in Computer Vision and image understanding, vol 114 n° 11 (November 2010)PermalinkAn integrated approach for visual analysis of a multisource moving objects knowledge base / N. Wllems in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)PermalinkA two-step displacement correction algorithm for registration of lidar point clouds and aerial images without orientation parameters / H. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 10 (October 2010)PermalinkRecalage de relevés laser fixes et mobiles sur MNS pour la cartographie numérique 3D / T. Ridene in Revue Française de Photogrammétrie et de Télédétection, n° 192 (Septembre 2010)PermalinkThe power of full motion vidéo: fusing with other intelligent data / L. Wood in Geoinformatics, vol 13 n° 2 (01/03/2010)PermalinkCollective detection: enhancing GNSS receiver sensitivity by combining signals from multiple satellites / Penina Axelrad in GPS world, vol 21 n° 1 (January 2010)PermalinkFusion d'images optique et radar à haute résolution pour la mise à jour de bases de données cartographiques / Vincent Poulain (2010)PermalinkPermalinkPré-étude pour la constitution d'une base de données d'occupation du sol / Clément Delgrange (2010)PermalinkAugmenting the Iterative Closest Point (ICP) alignment algorithm with intensity / S. Hefford in Geomatica, vol 63 n° 4 (December 2009)PermalinkFinding anomalies in high-density Lidar point clouds / J. Harrison in Geomatica, vol 63 n° 4 (December 2009)PermalinkImproving GPS localization with vision and inertial sensing / A. Fakih in Geomatica, vol 63 n° 4 (December 2009)Permalink