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Geometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)
[article]
Titre : Geometric features and their relevance for 3D point cloud classification Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Boris Jutzi, Auteur ; Clément Mallet , Auteur ; Michael Weinmann, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Article en page(s) : pp 157 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classe d'objets
[Termes IGN] classification
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage de données
[Termes IGN] étiquette de classe
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] interprétation automatique
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available. Numéro de notice : A2017-860 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-1-W1-157-2017 Date de publication en ligne : 30/05/2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89840
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-1/W1 (May 2017) . - pp 157 - 164[article]Self-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
[article]
Titre : Self-taught feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ronald Kemker, Auteur ; Christopher Kanan, Auteur Année de publication : 2017 Article en page(s) : pp 2693 - 2705 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. Numéro de notice : A2017-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2651639 En ligne : https://doi.org/10.1109/TGRS.2017.2651639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86390
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2693 - 2705[article]Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)
[article]
Titre : Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data Type de document : Article/Communication Auteurs : André Dittrich, Auteur ; Martin Weinmann, Auteur ; Stefan Hinz, Auteur Année de publication : 2017 Article en page(s) : pp 195 – 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bruit (théorie du signal)
[Termes IGN] calcul tensoriel
[Termes IGN] discrétisation
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] lasergrammétrie
[Termes IGN] méthode robuste
[Termes IGN] restitution lasergrammétrique
[Termes IGN] semis de points
[Termes IGN] valeur propreRésumé : (auteur) In photogrammetry, remote sensing, computer vision and robotics, a topic of major interest is represented by the automatic analysis of 3D point cloud data. This task often relies on the use of geometric features amongst which particularly the ones derived from the eigenvalues of the 3D structure tensor (e.g. the three dimensionality features of linearity, planarity and sphericity) have proven to be descriptive and are therefore commonly involved for classification tasks. Although these geometric features are meanwhile considered as standard, very little attention has been paid to their accuracy and robustness. In this paper, we hence focus on the influence of discretization and noise on the most commonly used geometric features. More specifically, we investigate the accuracy and robustness of the eigenvalues of the 3D structure tensor and also of the features derived from these eigenvalues. Thereby, we provide both analytical and numerical considerations which clearly reveal that certain features are more susceptible to discretization and noise whereas others are more robust. Numéro de notice : A2017-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.012 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84512
in ISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 195 – 208[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017041 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017043 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017042 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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Titre : Deep supervised and contractive neural network for SAR image classification Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Hongyu Wang, Auteur ; Jianchao Fan, Auteur ; Xiaorui Ma, Auteur Année de publication : 2017 Article en page(s) : pp 2442 - 2459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de déchatoiement
[Termes IGN] filtre de Gabor
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)Résumé : (Auteur) The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. Numéro de notice : A2017-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2645226 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2645226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84748
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2442 - 2459[article]Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification Type de document : Article/Communication Auteurs : Minchao Ye, Auteur ; Yuntao Qian, Auteur ; Jun Zhou, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2017 Article en page(s) : pp 1544 - 1562 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] régression logistiqueRésumé : (Auteur) A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification. Numéro de notice : A2017-157 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2627042 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2627042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84694
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1544 - 1562[article]Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery / Xiaobing Han in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)PermalinkBuilding occlusion detection from ghost images / Guoqing Zhou in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)PermalinkOn the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)PermalinkPermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkMise en place d’un processus de dessin automatisé de plans d’intérieurs à partir de nuages de points acquis par LIDAR / Léa Talec (2017)PermalinkRaft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features / Wang Min in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkPermalinkPermalinkClass-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkUrban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)PermalinkGuided superpixel method for topographic map processing / Qiguang Miao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkAutomatic registration of MLS point clouds and SfM meshes of urban area / Reiji Yoshimura in Geo-spatial Information Science, vol 19 n° 3 (October 2016)PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkA robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data / Hamid Hamraz in International journal of applied Earth observation and geoinformation, vol 52 (October 2016)PermalinkSAR image change detection based on correlation kernel and multistage extreme learning machine / Lu Jia in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkInternational benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning / Yunsheng Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkAutomatic delineation of built-up area at urban block level from topographic maps / Sebastian Muhs in Computers, Environment and Urban Systems, vol 58 (July 2016)PermalinkClassifying buildings from point clouds and images / Evangelos Maltezos in GIM international [en ligne], vol 30 n° 7 (July 2016)PermalinkRegistration-based mapping of aboveground disparities (RMAD) for building detection in off-nadir VHR stereo satellite imagery / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)Permalink