Centre dedocumentation
scientifique

Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 167 (September 2019) . - pp 266 - 281 Titre : Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data Type de document : Article/Communication Auteurs : Mateo Gašparović, Auteur ; Dino Dobrinić, Auteur ; Damir Medak, Auteur Année de publication : 2019 Article en page(s) : pp 266 - 281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orientation du capteur [Termes descripteurs IGN] orthorectification [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] précision géométrique (imagerie) Résumé : (auteur) This paper provides geometric accuracy improvements for very highresolution WorldView-2 (WV2) Ortho Ready Standard Level-2A (ORS2A) satelliteimages in the panchromatic (PAN) band. The entire process can be divided intotwo steps: (1) sensor orientation, which was conducted on 40 ground controlpoints (GCPs) to determine which sensor model was best depending on variousnumbers of GCPs; and (2) orthorectification, whose main objective was tocompare the accuracy of the process when using different freely available globaldigital elevation models (DEMs). The research showed that a sensor model basedon rational polynomial coefficients (RPCs) with a shift or zero-order biascorrection attained the best geopositioning accuracy during the orientation stepwith the use of seven GCPs. The highest accuracy was achieved for imagesorthorectified with the ALOS World 3D-30 m DEM (AW3D30). Numéro de notice : A2019-432 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12292 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93759 [article]

Vehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 4 (avril 2019) . - pp 297 - 304 Titre : Vehicle detection in aerial images Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Xinbo Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 297 - 304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] entropie [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] qualité d'image [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] véhicule automobile Résumé : (Auteur) The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection. Numéro de notice : A2019-163 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.4.297 date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.14358/PERS.85.4.297 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92568 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019041	SL	Revue	Centre de documentation	Revues en salle	Disponible

3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 8 (August 2018) . - pp 4420 - 4434 Titre : 3-D deep learning approach for remote sensing image classification Type de document : Article/Communication Auteurs : Amina Ben Hamida, Auteur ; Alexandre Benoit, Auteur ; Patrick Lambert, Auteur ; Chokri Ben Amar, Auteur Année de publication : 2018 Article en page(s) : pp 4420 - 4434 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] qualité géométrique (image) [Termes descripteurs IGN] valeur radiométrique Résumé : (Auteur) Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today's large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs. Numéro de notice : A2018-470 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2818945 date de publication en ligne : 20/04/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2818945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91252 [article]

Toward automatic georeferencing of archival aerial photogrammetric surveys / Sébastien Giordano in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2 ([28/05/2018])  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > IV-2 [28/05/2018] . - pp 105 - 112 Titre : Toward automatic georeferencing of archival aerial photogrammetric surveys Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Arnaud Le Bris , Auteur ; Clément Mallet , Auteur Congrès : ISPRS 2018, TC II Mid-term Symposium "Towards Photogrammetry 2020" (4 - 7 juin 2018; Riva del Garda, Italie) , Commanditaire Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 105 - 112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] archives [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orientation absolue [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] pas d'échantillonnage au sol [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] précision géométrique (imagerie) [Termes descripteurs IGN] série temporelle Résumé : (auteur) Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points. Numéro de notice : A2018-330 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-105-2018 date de publication en ligne : 28/05/2018 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-105-2018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90473 [article]

No-reference image quality assessment for image auto-denoising / Xiangfei Kong in International journal of computer vision, vol 126 n° 5 (May 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 5 (May 2018) . - pp 537 - 549 Titre : No-reference image quality assessment for image auto-denoising Type de document : Article/Communication Auteurs : Xiangfei Kong, Auteur ; Qingxiong Yang, Auteur Année de publication : 2018 Article en page(s) : pp 537 - 549 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] qualité d'image Résumé : (Auteur) This paper proposes two new non-reference image quality metrics that can be adopted by the state-of-the-art image/video denoising algorithms for auto-denoising. The first metric is proposed based on the assumption that the noise should be independent of the original image. A direct measurement of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus tackles the homogeneous regions and highly-structured regions separately. Nevertheless, this metric is only stable when the noise level is relatively low. Most denoising algorithms reduce noise by (weighted) averaging repeated noisy measurements. As a result, another metric is proposed for high-level noise based on the fact that more noisy measurements will be required when the noise level increases. The number of measurements before converging is thus related to the quality of noisy images. Our patch-matching based metric proposes to iteratively find and add noisy image measurements for averaging until there is no visible difference between two successively averaged images. Both metrics are evaluated on LIVE2 (Sheikh et al. in LIVE image quality assessment database release 2: 2013) and TID2013 (Ponomarenko et al. in Color image database tid2013: Peculiarities and preliminary results: 2005) data sets using standard Spearman and Kendall rank-order correlation coefficients (ROCC), showing that they subjectively outperforms current state-of-the-art no-reference metrics. Quantitative evaluation w.r.t. different level of synthetic noisy images also demonstrates consistently higher performance over state-of-the-art non-reference metrics when used for image denoising. Numéro de notice : A2018-409 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1054-2 date de publication en ligne : 17/11/2017 En ligne : https://doi.org/10.1007/s11263-017-1054-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90883 [article]

Des exigences techniques de qualité pour l'exploitation des images / Laurent Polidori in Géomètre, n° 2156 (mars 2018)

Permalink

Foveon VS Bayer: Comparison of 3D reconstruction performances / Pierre Bodin (2018) 

Permalink

GNSS-assisted accurate corridor mapping with small UAV / Yilin Zhou (2018)    

Permalink

Large-scale block adjustment without use of ground control points based on the compensation of geometric calibration for ZY-3 images / Yang Bo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

Permalink

Estudio de precision en la aerotriangulacion de bloques de imagenes obtenidas con UAV / Miguel Angel Lopez Gonzalez in Mapping : Teledetección, medio ambiante, cartografía, sistemas de información geográfica, vol 26 n° 185 (septembrie - octubre 2017)    

Permalink

Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Permalink

Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)  

Permalink

Geometric accuracy evaluation of YG-18 satellite imagery based on RFM / Ruishan Zhao in Photogrammetric record, vol 32 n° 157 (March - May 2017)  

Permalink

Quality assessment methodologies of post-processed images / Lukas Krasula (2017)  

Permalink

Accuracy of tree geometric parameters depending on the LiDAR data density / Edyta Hadas in European journal of remote sensing, vol 49 (2016)  

Permalink