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Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
[article]
Titre : Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning Type de document : Article/Communication Auteurs : Benjamin Kellenberger, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2019 Article en page(s) : pp 9524 - 9533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] données localisées
[Termes IGN] échantillonnage de données
[Termes IGN] faune locale
[Termes IGN] image captée par drone
[Termes IGN] Namibie
[Termes IGN] objet mobile
[Termes IGN] réalité de terrain
[Termes IGN] recensementRésumé : (auteur) We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can be reused for repeated acquisitions, e.g., in follow-up years. Domain shifts between data sets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport (OT) to find corresponding regions between the source and the target data sets in the space of CNN activations. The CNN scores in the source data set are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target data set. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing quick retrieval of true positives in the target data set, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin. Numéro de notice : A2019-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927393 Date de publication en ligne : 20/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2927393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94592
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9524 - 9533[article]Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)
[article]
Titre : Scene context-driven vehicle detection in high-resolution aerial images Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Li Mi, Auteur ; Yansheng Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7339 - 7351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification orientée objet
[Termes IGN] détection d'objet
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] objet mobile
[Termes IGN] véhicule automobileRésumé : (auteur) As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. Numéro de notice : A2019-535 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2912985 Date de publication en ligne : 03/06/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2912985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94131
in IEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7339 - 7351[article]Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
[article]
Titre : Delineation of vacant building land using orthophoto and lidar data object classification Type de document : Article/Communication Auteurs : Dejan Jenko, Auteur ; Mojca Foški, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification orientée objet
[Termes IGN] couche thématique
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] logement
[Termes IGN] orthoimage
[Termes IGN] SlovénieRésumé : (Auteur) Exact data about the location and area of vacant building land have been a major issue in several Slovene municipalities. This article deals with automatic vacant building land delineation. The presented methodology is based on the object-based classification that derives the land cover layer from orthophoto and laser scanning data. With post-processing and data cleaning in GIS, we create the vacant building land layer. The methodology was tested in study areas in the Municipality of Trebnje. The results were compared to the vacant building land layer generated by visual interpretation (manual vectorisation). We found that the presented methodology of automatic delineation of vacant buildings can speed up the processing and lower the cost of manual vectorisation and, in particular, data updating but we cannot completely replace manual work. Numéro de notice : A2019-500 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.344-378 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.344-378 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93782
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 344 - 378[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)
[article]
Titre : Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data Type de document : Article/Communication Auteurs : Alexander Cass, Auteur ; George P. Petropoulos, Auteur ; Konstantinos P. Ferentinos, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 277 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie thématique
[Termes IGN] classification orientée objet
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Landsat-TM
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] Pays de Galles
[Termes IGN] surface cultivéeRésumé : (Auteur) Earth Observation (EO) provides a unique means of obtaining information on land use/cover and of its changes, which is of key importance in many scientific and practical applications. EO data is already widely used, for example, in environmental practices or decision-making related to food availability and security. As such, it is imperative to examine the suitability of different EO datasets, including their synergies, in respect to their ability to create products and tools for such practices and to guide effectively such decisions. This work aims at exploring the added value of the synergistic use of optical and radar data (from the Landsat TM and Advanced Synthetic Aperture Radar (ASAR) sensors respectively). Such information can help towards improving the accuracy of land cover classifications from EO datasets. As a case study, the region of Wales in the UK has been used. Two classifications—one based on optical data alone and another one developed from the synergy of optical and RADAR datasets acquired nearly, concurrently were developed for the studied region. Evaluation of the derived land/use cover maps was performed on the basis of the confusion matrix using validation points derived from a Phase 1 habitat map of Wales. The results showed 15% increase in overall accuracy (84% from 69%) and kappa coefficient (0.81 from 0.65) using the synergistic approach over the scenario where only optical data were used in the classification. In addition, McNemar’s test was used to assess the statistical significance of the obtained results. Results of this test provided further confirmed that the use of optical data synergistically with the radar data provides more accurate land use/cover maps in comparison with the use of optical data alone. Numéro de notice : A2019-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00258-7 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1007/s12518-019-00258-7 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93601
in Applied geomatics > vol 11 n° 3 (September 2019) . - pp 277 - 288[article]A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)
[article]
Titre : A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm Type de document : Article/Communication Auteurs : Ana Claudia Dos Santos Luciano, Auteur ; Michelle Cristina Araújo Picoli, Auteur ; Jansle Vieira Rocha, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 127-136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] apprentissage automatique
[Termes IGN] Brésil
[Termes IGN] carte agricole
[Termes IGN] classification dirigée
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image Landsat
[Termes IGN] production agricole
[Termes IGN] Saccharum officinarum
[Termes IGN] série temporelle
[Termes IGN] surface cultivée
[Termes IGN] zone d'intérêtRésumé : (auteur) The monitoring of sugarcane areas is important for sustainable planning and management of the sugarcane industry in Brazil. We developed an operational Object-Based Image Analysis (OBIA) classification scheme, with generalized space-time classifier, for mapping sugarcane areas at the regional scale in São Paulo State (SP). Binary random forest (RF) classification models were calibrated using multi-temporal data from Landsat images, at 10 sites located across SP. Space and time generalization were tested and compared for three approaches: a local calibration and application; a cross-site spatial generalization test with the RF model calibrated on a site and applied on other sites; and a unique space–time classifier calibrated with all sites together on years 2009–2014 and applied to the entire SP region on 2015. The local RF models Dice Coefficient (DC) accuracies at sites 1 to 8 were between 0.83 and 0.92 with an average of 0.89. The cross-site classification accuracy showed an average DC of 0.85, and the unique RF model had a DC of 0.89 when compared with a reference map of 2015. The results demonstrated a good relationship between sugarcane prediction and the reference map for each municipality in SP, with R² = 0.99 and only 5.8% error for the total sugarcane area in SP, and compared with the area inventory from the Brazilian Institute of Geography and Statistics, with R² = 0.95 and –1% error for the total sugarcane area in SP. The final unique RF model allowed monitoring sugarcane plantations at the regional scale on independent year, with efficiency, low-cost, limited resources and a precision approximating that of a photointerpretation. Numéro de notice : A2019-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.04.013 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.jag.2019.04.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93612
in International journal of applied Earth observation and geoinformation > vol 80 (August 2019) . - pp 127-136[article]3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkA light and faster regional convolutional neural network for object detection in optical remote sensing images / Peng Ding in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkAn 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)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)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)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)PermalinkRobust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 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)PermalinkUnsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)PermalinkContributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)PermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (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)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)PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkA multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkClassifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkTraining set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)PermalinkCoregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)Permalink