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Application of deep learning for object detection / Ajeet Ram Pathak in Procedia Computer Science, vol 132 (2018)
[article]
Titre : Application of deep learning for object detection Type de document : Article/Communication Auteurs : Ajeet Ram Pathak, Auteur ; Manjusha Pandey, Auteur ; Siddharth Rautaray, Auteur Année de publication : 2018 Article en page(s) : pp 1706 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] état de l'art
[Termes IGN] réseau neuronal convolutif
[Termes IGN] vision par ordinateurRésumé : (auteur) The ubiquitous and wide applications like scene understanding, video surveillance, robotics, and self-driving systems triggered vast research in the domain of computer vision in the most recent decade. Being the core of all these applications, visual recognition systems which encompasses image classification, localization and detection have achieved great research momentum. Due to significant development in neural networks especially deep learning, these visual recognition systems have attained remarkable performance. Object detection is one of these domains witnessing great success in computer vision. This paper demystifies the role of deep learning techniques based on convolutional neural network for object detection. Deep learning frameworks and services available for object detection are also enunciated. Deep learning techniques for state-of-the-art object detection systems are assessed in this paper. Numéro de notice : A2018-585 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.procs.2018.05.144 Date de publication en ligne : 08/06/2018 En ligne : https://www.sciencedirect.com/science/article/pii/S1877050918308767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92435
in Procedia Computer Science > vol 132 (2018) . - pp 1706 - 1717[article]An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)
[article]
Titre : An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery Type de document : Article/Communication Auteurs : Luis Angel Ruiz, Auteur ; Jorge Abel Recio, Auteur ; Pablo Crespo-Peremarch, Auteur ; Marta Sapena Moll, Auteur Année de publication : 2018 Article en page(s) : pp 443 - 457 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre de décision
[Termes IGN] biomasse (combustible)
[Termes IGN] carte forestière
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt méditerranéenne
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. Numéro de notice : A2018-140 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265595 Date de publication en ligne : 28/11/2016 En ligne : https://doi.org/10.1080/10106049.2016.1265595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89690
in Geocarto international > vol 33 n° 5 (May 2018) . - pp 443 - 457[article]Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)
[article]
Titre : Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Amr Abd-Elrahman, Auteur Année de publication : 2018 Article en page(s) : pp 154 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] drone
[Termes IGN] orthoimage
[Termes IGN] réseau neuronal convolutif
[Termes IGN] zone humideRésumé : (Auteur) Deep convolutional neural network (DCNN) requires massive training datasets to trigger its image classification power, while collecting training samples for remote sensing application is usually an expensive process. When DCNN is simply implemented with traditional object-based image analysis (OBIA) for classification of Unmanned Aerial systems (UAS) orthoimage, its power may be undermined if the number training samples is relatively small. This research aims to develop a novel OBIA classification approach that can take advantage of DCNN by enriching the training dataset automatically using multi-view data. Specifically, this study introduces a Multi-View Object-based classification using Deep convolutional neural network (MODe) method to process UAS images for land cover classification. MODe conducts the classification on multi-view UAS images instead of directly on the orthoimage, and gets the final results via a voting procedure. 10-fold cross validation results show the mean overall classification accuracy increasing substantially from 65.32%, when DCNN was applied on the orthoimage to 82.08% achieved when MODe was implemented. This study also compared the performances of the support vector machine (SVM) and random forest (RF) classifiers with DCNN under traditional OBIA and the proposed multi-view OBIA frameworks. The results indicate that the advantage of DCNN over traditional classifiers in terms of accuracy is more obvious when these classifiers were applied with the proposed multi-view OBIA framework than when these classifiers were applied within the traditional OBIA framework. Numéro de notice : A2018-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.03.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.03.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89550
in ISPRS Journal of photogrammetry and remote sensing > vol 139 (May 2018) . - pp 154 - 170[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data / Razieh Shojanoori in Geocarto international, vol 33 n° 4 (April 2018)
[article]
Titre : Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data Type de document : Article/Communication Auteurs : Razieh Shojanoori, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Shattri Bin Mansor, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 357 - 374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre (flore)
[Termes IGN] arbre urbain
[Termes IGN] base de règles
[Termes IGN] détection d'arbres
[Termes IGN] image Worldview
[Termes IGN] Malaisie
[Termes IGN] traitement d'image
[Termes IGN] zone urbaineRésumé : (Auteur) The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model. Numéro de notice : A2018-046 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265593 En ligne : https://doi.org/10.1080/10106049.2016.1265593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89268
in Geocarto international > vol 33 n° 4 (April 2018) . - pp 357 - 374[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2018021 RAB Revue Centre de documentation En réserve L003 Disponible Active learning-based optimized training library generation for object-oriented image classification / Rajeswari Balasubramaniam in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
[article]
Titre : Active learning-based optimized training library generation for object-oriented image classification Type de document : Article/Communication Auteurs : Rajeswari Balasubramaniam, Auteur ; Srivalsan Namboodiri, Auteur ; Rama Rao Nidamanuri, Auteur ; Rama Krishna Sai Subrahmanyam Gorthi, Auteur Année de publication : 2018 Article en page(s) : pp 575 - 585 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image aérienne
[Termes IGN] image multibandeRésumé : (Auteur) In this paper, we introduce an active learning (AL)-based object training library generation for a multiclassifier object-oriented image analysis (OOIA) system. While several AL approaches do exist for pixel-based training library generation and for hyperspectral image classification, there is no standard training library generation strategy for OOIA of very high spatial resolution images. Given a sufficient number of training samples, supervised classification is the method of choice for image classification. However, this strategy becomes computationally expensive with the increase in the number of classes or the number of images to be classified. The above-mentioned issue is solved in this proposed method, where an optimized training library of objects (superpixels) is generated based on a batch mode AL approach. A softmax classifier is used as a detector in this method, which helps in determining the right samples to be chosen for library updation. To this end, we construct a multiclassifier system with max-voting decision to classify an image at pixel level. This algorithm was applied on three different very high-resolution airborne data sets, each with varying complexity in terms of variations in geographical context, sensors, illumination, and view angles. Our method has empirically outperformed the traditional OOIA by producing equivalent accuracy with a training library that is orders of magnitude smaller. In addition, the most distinctive ability of the algorithm is experienced in the most heterogeneous data set, where its performance in terms of accuracy is around twice the performance of the traditional method in the same situation. The generality of this classification strategy is proved through its performance on multispectral images and for cross-domain application. Finally, the robustness of this method is identified by comparing its performance with an alternative AL approach-self-learning-based semisupervised SVM. The capability of the proposed method to handle highly heterogeneous data is identified as the primary reason for its robustness. Numéro de notice : A2018-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2751568 Date de publication en ligne : 29/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2751568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89847
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 575 - 585[article]Adapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkAn (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018)PermalinkComparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs / Abraham Montoya Obeso (2018)PermalinkPermalinkFacade repetition detection in a fronto-parallel view with fiducial lines extraction / Hongfei Xiao in Neurocomputing, vol 273 (January 2018)PermalinkObject-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)PermalinkRéseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)PermalinkUtilisation de QGIS en télédétection, Ch. 2. Apports du MNT topo-bathymétrique pour l'évolution bio-géomorphologique des marais d'Ichkeul (Tunisie) / Zeineb Kassouk (2018)PermalinkUtilisation de véhicules traceurs pour la détection et la localisation de l'infrastructure routière par apprentissage automatique / Yann Méneroux (2018)PermalinkObject-based classification of terrestrial laser scanning point clouds for landslide monitoring / Andreas Mayr in Photogrammetric record, vol 32 n° 160 (December 2017)PermalinkExtraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)PermalinkTowards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions / Elisabeth Schoepfer in Geocarto international, vol 32 n° 10 (October 2017)PermalinkOccupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)PermalinkA graph-based approach to detect spatiotemporal dynamics in satellite image time series / Fabio Guttler in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkLocal and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkRobust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkEstimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data / Joachim Maack in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkObject-based analysis of multispectral airborne laser scanner data for land cover classification and map updating / Leena Matikainen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkSemiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment / Javier Sanchez in Journal of Cultural Heritage, vol 25 (May - June 2017)PermalinkApplying detection proposals to visual tracking for scale and aspect ratio adaptability / Dafei Huang in International journal of computer vision, vol 122 n° 3 (May 2017)PermalinkMise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkA classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas / Martin Weinmann in Remote sensing, vol 9 n° 3 (March 2017)PermalinkIndustrialisation des processus d'extraction d'objets à partir de données photogrammétriques par drones / Jérémie Brossard in XYZ, n° 150 (mars - mai 2017)PermalinkUnsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)PermalinkAgricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests / M.F.A. Vogels in International journal of applied Earth observation and geoinformation, vol 54 (February 2017)PermalinkObject-based water body extraction model using Sentinel-2 satellite imagery / Gordana Kaplan in European journal of remote sensing, vol 50 n° 1 (2017)PermalinkPermalinkCartographie et interprétation de l'environnement par drone / Martial Sanfourche in Revue Française de Photogrammétrie et de Télédétection, n° 213 - 214 (janvier - avril 2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkPermalinkPermalinkPermalinkPermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)PermalinkThe use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)PermalinkA two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)PermalinkUrban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)PermalinkUtilisation de données satellites dans le combat contre l'esclavage moderne / Florent Negrel-Teodori (2017)PermalinkPermalinkVision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)PermalinkMapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest / Iurii Shendryk in Remote sensing of environment, vol 187 (15 December 2016)Permalink