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What, where, and how to transfer in SAR target recognition based on deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
[article]
Titre : What, where, and how to transfer in SAR target recognition based on deep CNNs Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 2324 - 2336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] données multisources
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] source de données
[Termes IGN] transmission de donnéesRésumé : (auteur) Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, we attempt to discuss three issues that are seldom studied before in detail: 1) what network and source tasks are better to transfer to SAR targets; 2) in which layer are transferred features more generic to SAR targets; and 3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multisource data with domain adaptation is proposed in this article to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Numéro de notice : A2020-195 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947634 Date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947634 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94863
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336[article]Mise en place d'un système d’auscultation par photogrammétrie aérienne et comparaison avec un scanner laser 3D / Benoît Brizard (2020)
Titre : Mise en place d'un système d’auscultation par photogrammétrie aérienne et comparaison avec un scanner laser 3D Type de document : Mémoire Auteurs : Benoît Brizard, Auteur Editeur : Le Mans : Ecole Supérieure des Géomètres et Topographes ESGT Année de publication : 2020 Importance : 73 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire présenté en vue d'obtenir le diplome d'Ingénieur CNAM Spécialité Géomètre et TopographeLangues : Français (fre) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] acquisition de données
[Termes IGN] analyse comparative
[Termes IGN] auscultation d'ouvrage
[Termes IGN] CloudCompare
[Termes IGN] déformation d'édifice
[Termes IGN] détection de cible
[Termes IGN] données localisées 3D
[Termes IGN] géoréférencement
[Termes IGN] photogrammétrie aérienne
[Termes IGN] semis de pointsIndex. décimale : ESGT Mémoires d'ingénieurs de l'ESGT Résumé : (auteur) À travers un processus photogrammétrique maîtrisé, depuis la prise des photographies à la génération du nuage de points dense avec le logiciel Metashape, ce dernier devient un support de mesure pour pratiquer des auscultations sur des ouvrages. L’algorithme M3C2, implémenté dans le logiciel CloudCompare, permet d’effectuer des calculs de déplacement entre deux nuages de points. Les incertitudes de mesures inhérentes sont considérées dans ce calcul et permettent de produire des cartes des déplacements au seuil de confiance de 95 %. La précision de détection obtenue est de l'ordre de quelques millimètres lors d’une application à un relevé aérien. Cela démontre le potentiel de la photogrammétrie comme moyen d’auscultation précis. Note de contenu : Introduction
1- L'auscultation par photogrammétrie
2- Application à la photogrammétrie terrestre
3- Application à un levé aérien par drone
ConclusionNuméro de notice : 28642 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Mémoire ingénieur ESGT Organisme de stage : Cementys DOI : sans En ligne : https://dumas.ccsd.cnrs.fr/dumas-03113461/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99731
Titre : Remote sensing for target object detection and identification Type de document : Monographie Auteurs : Gemine Vivone, Éditeur scientifique ; Paolo Addesso, Éditeur scientifique ; Amanda Ziemann, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 336 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03928-333-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] image radar moirée
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] surveillance écologiqueRésumé : (éditeur) Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amount of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. This book promotes papers exploiting different remote sensing data for target object detection and identification, such as synthetic aperture radar (SAR) imaging and multispectral and hyperspectral imaging. Several cutting-edge contributions, which provide examples of how to select of a technology or another depending on the specific application, will be detailed. Note de contenu : Editorial
1- Pixel tracking to estimate rivers water flow elevation using Cosmo-SkyMed synthetic aperture radar data
2- Flood distance algorithms and fault hidden danger recognition for transmission line towers based on SAR Images
3- Geospatial object detection on high resolution remote sensing imagery based on double multi-scale feature pyramid network
4- A novel multi-model decision fusion network for object detection in remote sensing images
5- Local region proposing for frame-based vehicle detection in satellite videos
6- Efficient object detection framework and hardware architecture for remote sensing images
7- Unsupervised saliency model with color Markov chain for oil tank detection
8- Affine-function transformation-based object matching for vehicle detection from unmanned aerial vehicle imagery
9- Hyperspectral anomaly detection via dictionary construction-based low-rank representation and adaptive weighting
10- Infrared small target detection based on non-convex optimization with Lp-Norm constraint
11- Infrared small target detection based on partial sum of the tensor nuclear norm
12- Infrared small-faint target detection using non-i.i.d. mixture of Gaussians and flux density
13- Mask sparse representation based on semantic features for thermal infrared target tracking
14- Infrared optical observability of an earth entry orbital test vehicle using ground-based remote sensorsNuméro de notice : 25885 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie DOI : 10.3390/books978-3-03928-333-0 En ligne : https://doi.org/10.3390/books978-3-03928-333-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95781 Underwater field equipment of a network of landmarks optimized for automatic detection by AI / Laurent Beaudoin (2020)
Titre : Underwater field equipment of a network of landmarks optimized for automatic detection by AI Type de document : Article/Communication Auteurs : Laurent Beaudoin, Auteur ; Loïca Avanthey, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Conférence : IGARSS 2020, 2020 IEEE International Geoscience and Remote Sensing Symposium 26/09/2020 02/10/2020 Waikoloa, Hawaï Etats-Unis proceedings IEEE Importance : n° 9323589 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] détection automatique
[Termes IGN] détection de cible
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3DRésumé : (auteur) To qualify the point clouds obtained by 3D reconstruction of a global study area in close-range remote sensing, control points, whose position has been measured essentially manually in the field with an instrument whose precision is known, are used. In the underwater environment, equipping the field and carrying out these measurements is a complex operation to perform due to the peculiarities of the environment. We present in this article a first step towards the automation of this task, the automatic detection of targets by a deep learning algorithm which will serve to correctly position the control points locally, and a simplification of the manual measurement which will serve in future work to control the results of automatic readings. Numéro de notice : C2020-040 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS39084.2020.9323589 En ligne : https://doi.org/10.1109/IGARSS39084.2020.9323589 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102626 Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
[article]
Titre : Ship identification and characterization in Sentinel-1 SAR images with multi-task deep learning Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Sébastien Lefèvre, Auteur ; Rodolphe Vadaine, Auteur ; Guillaume Hajduch, Auteur ; Ronan Fablet, Auteur Année de publication : 2019 Projets : SESAME / Fablet, Ronan Article en page(s) : n° 2997 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image Sentinel-SAR
[Termes IGN] navire
[Termes IGN] objet mobileRésumé : (auteur) The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m). Numéro de notice : A2019-632 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11242997 Date de publication en ligne : 13/12/2019 En ligne : https://doi.org/10.3390/rs11242997 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95325
in Remote sensing > Vol 11 n° 24 (December-2 2019) . - n° 2997[article]Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkEmpirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkReal-time relative mobile target positioning using GPS-assisted stereo videogrammetry / Bahadir Ergun in Survey review, vol 50 n° 361 (July 2018)PermalinkPermalinkBand subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkChange detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications / Zhe Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkExtracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkImage-based target detection and radial velocity estimation methods for multichannel SAR-GMTI / Kei Suwa in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkJoint sparse representation and multitask learning for hyperspectral target detection / Yuxiang Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 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)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)PermalinkSemi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (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)PermalinkUse of doppler parameters for ship velocity computation in SAR images / Alfredo Renga in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkAutomated detection of Martian gullies from HiRISE imagery / Wei Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)PermalinkMAGI : A new high-performance airborne thermal-infrared imaging spectrometer for earth science applications / Jeffrey L. Hall in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkProgressive band processing of constrained energy minimization for subpixel detection / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkTarget identification in terrestrial laser scanning / Xuming Ge in Survey review, vol 47 n° 341 (March 2015)PermalinkEvaluating tree detection and segmentation routines on very high resolution UAV LiDAR data / Luke Wallace in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)Permalink