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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)
[article]
Titre : Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery Type de document : Article/Communication Auteurs : Ruchan Dong, Auteur ; Dazhuan Xu, Auteur ; Jin Zhao, Auteur ; Licheng Jiao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 8534 - 8545 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] détection de cible
[Termes IGN] image à très haute résolution
[Termes IGN] régression
[Termes IGN] zone d'intérêtRésumé : (auteur) Small target detection is a challenging task in veryhigh-resolution (VHR) optical remote sensing imagery, because small targets occupy a minuscule number of pixels and are easily disturbed by backgrounds or occluded by others. Although current convolutional neural network (CNN)-based approaches perform well when detecting normal objects, they are barely suitable for detecting small ones. Two practical problems stand in their way. First, current CNN-based approaches are not specifically designed for the minuscule size of small targets (~15 or ~10 pixels in extent). Second, no well-established data sets include labeled small targets and establishing one from scratch is labor-intensive and time-consuming. To address these two issues, we propose an approach that combines Sig-NMS-based Faster R-CNN with transfer learning. Sig-NMS replaces traditional non-maximum suppression (NMS) in the stage of region proposal network and decreases the possibility of missing small targets. Transfer learning can effectively label remote sensing images by automatically annotating both object classes and object locations. We conduct an experiment on three data sets of VHR optical remote sensing images, RSOD, LEVIR, and NWPU VHR-10, to validate our approach. The results demonstrate that the proposed approach can effectively detect small targets in the VHR optical remote sensing images of about 10 × 10 pixels and automatically label small targets as well. In addition, our method presents better mean average precisions than other state-of-the-art methods: 1.5% higher when performing on the RSOD data set, 17.8% higher on the LEVIR data set, and 3.8% higher on NWPU VHR-10. Numéro de notice : A2019-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2921396 Date de publication en ligne : 15/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2921396 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94587
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8534 - 8545[article]Empirical 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)
[article]
Titre : Empirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners Type de document : Article/Communication Auteurs : Tomislav Medic, Auteur ; Christoph Holst, Auteur ; Jannik Janssen, Auteur ; Heiner Kuhlmann, Auteur Année de publication : 2019 Article en page(s) : pp 179 – 197 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] centroïde
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection de cible
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage d'instrument
[Termes IGN] incertitude de mesurage
[Termes IGN] métrologie dimensionelle
[Termes IGN] modèle stochastique
[Termes IGN] semis de points
[Termes IGN] télémètre laser terrestreRésumé : (auteur) The target-based point cloud registration and calibration of terrestrial laser scanners (TLSs) are mathematically modeled and solved by the least-squares adjustment. However, usual stochastic models are simplified to a large amount: They generally employ a single point measurement uncertainty based on the manufacturers’ specifications. This definition does not hold true for the target-based calibration and registration due to the fact that the target centroid is derived from multiple measurements and its uncertainty depends on the detection procedure as well. In this study, we empirically investigate the precision of the target centroid detection and define an empirical stochastic model in the form of look-up tables. Furthermore, we compare the usual stochastic model with the empirical stochastic model on several point cloud registration and TLS calibration experiments. There, we prove that the values of usual stochastic models are underestimated and incorrect, which can lead to multiple adverse effects such as biased results of the estimation procedures, a false a posteriori variance component analysis, false statistical testing, and false network design conclusions. In the end, we prove that some of the adverse effects can be mitigated by employing the a priori knowledge about the target centroid uncertainty behavior. Numéro de notice : A2019-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0032 Date de publication en ligne : 22/03/2019 En ligne : https://doi.org/10.1515/jag-2018-0032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93119
in Journal of applied geodesy > vol 13 n° 3 (July 2019) . - pp 179 – 197[article]Real-time relative mobile target positioning using GPS-assisted stereo videogrammetry / Bahadir Ergun in Survey review, vol 50 n° 361 (July 2018)
[article]
Titre : Real-time relative mobile target positioning using GPS-assisted stereo videogrammetry Type de document : Article/Communication Auteurs : Bahadir Ergun, Auteur ; Irfan Sayim, Auteur ; Cumhur Sahin, Auteur ; N. Tok, Auteur Année de publication : 2018 Article en page(s) : pp 326 - 335 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] détection de cible
[Termes IGN] image vidéo
[Termes IGN] photogrammétrie métrologique
[Termes IGN] VidéogrammétrieRésumé : (Auteur) Positioning of a GPS-equipped (Global Positioning System) moving target was determined by stereo-videogrammetry from two images of cameras where they were placed on another GPS-equipped moving platform. The computed position outputs of target were compared with the relative positions obtained from two GPS receivers. The target, a small square-like pattern, was tracked from a certain distance depending on the base distance between the cameras. The video files were created from acquired images data. These video files were used in real-time computation to get the target image position for every film frame. First, the location of target was computed within video film frames. Since the target cannot be searched on the whole picture, maximum pixel length, which the target can travel on the consecutive film frames was considered as offset. Therefore, the search was made over a small area rather than whole picture. That was improved the performance of positioning. Finally, videogrammetrically computed coordinates for all epochs were compared with GPS-based relative distances to justify performance of relative target positioning results. Numéro de notice : A2018-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2016.1267303 Date de publication en ligne : 27/12/2016 En ligne : https://doi.org/10.1080/00396265.2016.1267303 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91015
in Survey review > vol 50 n° 361 (July 2018) . - pp 326 - 335[article]
Titre : UAV sensors for environmental monitoring Type de document : Monographie Auteurs : Felipe Gonzalez Toro, Éditeur scientifique ; Antonios Tsourdos, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2018 Importance : 660 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03842-753-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] accident de la route
[Termes IGN] biodiversité
[Termes IGN] capteur aérien
[Termes IGN] capteur terrestre
[Termes IGN] détection de cible
[Termes IGN] filtre passe-bande
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] instrument embarqué
[Termes IGN] photogrammétrie aérienne
[Termes IGN] prévention des risques
[Termes IGN] surveillance écologique
[Termes IGN] surveillance hydrologiqueRésumé : (éditeur) The rapid development and growth of UAVs as a remote sensing platform, as well as advances in the miniaturization of instrumentation and data systems, are catalyzing a renaissance in remote sensing in a variety of fields and disciplines from precision agriculture to ecology, atmospheric research, and disaster response.
This Special Issue was open for submissions that highlight advances in the development and use of sensors deployed on UAVs. Topics include, but were not limited, to:
- Optical, multi-spectral, hyperspectral, laser, and optical SAR technologies
- Gas analyzers and sensors
- Artificial intelligence and data mining based strategies from UAVs
- UAV onboard data storage, transmission, and retrieval
- Collaborative strategies and mechanisms to control multiple UAVs and sensor networks
- UAV sensor applications: precision agriculture; pest detection, forestry, mammal species tracking search and rescue; target tracking, the monitoring of the atmosphere; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring, pollution monitoring, micro-climates and land useNote de contenu : Preface
1- UAV-based photogrammetry and integrated technologies for architectural applications—methodological strategies for the after-quake survey of vertical structures in Mantua (Italy)
2- Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open-pit mine sites
3- Multi-UAV routing for area coverage and remote sensing with minimum time
4- UAV deployment exercise for mapping purposes: evaluation of emergency
response applications
5- Automated identification of river hydromorphological features using UAV high
resolution aerial imagery
6- Autonomous aerial refueling ground test demonstration—a sensor-in-the-loop,
non-tracking method
7- A new calibration method using low cost MEM IMUs to verify the performance of
UAV-borne MMS payloads
8- Adaptive environmental source localization and tracking with unknown permittivity and pathloss coefficients
9- Vision-based detection and distance estimation of micro unmanned aerial vehicles
10- Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation
11- UAVs task and motion planning in the presence of obstacles and prioritized targets
12- Towards the development of a smart flying sensor: Illustration in the field of
precision agriculture
13- Flight test result for the ground-based radio navigation system sensor with an
unmanned air vehicle
12- Multisensor super resolution using directionally-adaptive regularization for UAV images
13- UAV control on the basis of 3D landmark bearing-only observations
14- Cooperative surveillance and pursuit using unmanned aerial vehicles and unattended ground sensors
15- A multispectral image creating method for a new airborne four-camera system with
different bandpass filters
16- Vision and control for UAVs: A survey of general methods and of inexpensive platforms for infrastructure inspection
17- Feasibility of using synthetic aperture radar to aid UAV navigation
18- Towards an autonomous vision-based unmanned aerial system against wildlife poachers
19- Formation flight of multiple UAVs via onboard sensor information sharing
20- Mini-UAV based sensory system for measuring environmental variables in greenhouses
21- Mini-UAV based sensory system for measuring environmental variables in greenhouses
22- Dual-stack single-radio communication architecture for UAV acting as a mobile node to collect data in WSNs
23- Development and evaluation of a UAV-photogrammetry system for precise 3D
environmental modeling
24- Prototyping a GNSS-based passiveRadar for UAVs: An instrument to classify the
waterContent feature of lands
25- Enabling UAV navigation with sensor and environmental uncertainty in cluttered
and GPS-denied environments
26- UAV-based estimation of carbon exports from heterogeneous soil landscapes—a case
study from the carboZALF experimental area
27- Wavelength-adaptive dehazing using histogram merging-based classification for
UAV images
28- A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle
Routing in Traffic Incident Monitoring ApplicationsNuméro de notice : 25930 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03842-754-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96199 Band subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
[article]
Titre : Band subset selection for anomaly detection in hyperspectral imagery Type de document : Article/Communication Auteurs : Lin Wang, Auteur ; Chein-I Chang, Auteur ; Li-Chien Lee, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4887 - 4898 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'anomalie
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] jeu de donnéesRésumé : (Auteur) This paper presents a new approach, called band subset selection (BSS)-based hyperspectral anomaly detection (AD), which selects multiple bands simultaneously as a band subset rather than selecting multiple bands one at a time as the tradition band selection (BS) does, referred to as sequential multiple BS (SQMBS). Its idea is to first use virtual dimensionality (VD) to determine the number of multiple bands, nBS needed to be selected as a band subset and then develop two iterative process, sequential BSS (SQ-BSS) algorithm and successive BSS (SC-BSS) algorithm to find an optimal band subset numerically among all possible nBS combinations out of the full band set. In order to terminate the search process the averaged least-squares error (ALSE) and 3-D receiver operating characteristic (3D ROC) curves are used as stopping criteria to evaluate performance relative to AD using the full band set. Experimental results demonstrate that BSS generally performs better background suppression while maintaining target detection capability compared to target detection using full band information. Numéro de notice : A2017-658 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2681278 En ligne : https://doi.org/10.1109/TGRS.2017.2681278 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87069
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4887 - 4898[article]Change 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)Permalink