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Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
[article]
Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] appariement de formes
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] gradient
[Termes IGN] vision par ordinateur
[Termes IGN] vision stéréoscopiqueRésumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas.
To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas.
We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods.Numéro de notice : A2019-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 Date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729
in ISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
[article]
Titre : Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods Type de document : Article/Communication Auteurs : Jiří Šandera, Auteur ; Přemysl Štych, Auteur Année de publication : 2019 Article en page(s) : pp 379 - 394 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] carte d'occupation du sol
[Termes IGN] chaîne de traitement
[Termes IGN] changement d'occupation du sol
[Termes IGN] détection de changement
[Termes IGN] image Landsat
[Termes IGN] prairie
[Termes IGN] terre arableRésumé : (Auteur) The necessity of mapping changes in land cover categories based on satellite imageries is a challenging task especially in terms of arable land and grasslands. The phenological phases of arable lands change quickly while grasslands is more stable. It might be hard to capture these changes regarding the spectral overlap between crops in full growth and grass itself. We have introduced a relatively simple processing workflow with good efficiency and accuracy. Our proposed method utilises the combination of a Multivariate Alteration Change Detection Algorithm and an existing boosting method, such as the AdaBoost algorithm with different weak learners and the most recent one – Extreme Gradient Boosting that is actually a relatively new approach in remote sensing. According to the results, the highest overall accuracy is 89.51 %. The proposed process workflow was tested on Landsat data with 30 m spatial resolution, using open-source software: R and GRASS GIS, Orfeo Toolbox library. Numéro de notice : A2019-501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.379-394 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.379-394 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93783
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 379 - 394[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible Learning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
[article]
Titre : Learning and adapting robust features for satellite image segmentation on heterogeneous data sets Type de document : Article/Communication Auteurs : Sina Ghassemi, Auteur ; Attilio Friandrotti, Auteur ; Gianluca Francini, Auteur ; Enrico Magli, Auteur Année de publication : 2019 Article en page(s) : pp 6517 - 6529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] coût
[Termes IGN] données hétérogènes
[Termes IGN] image binaire
[Termes IGN] image satellite
[Termes IGN] méthode robuste
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation binaire
[Termes IGN] segmentation d'image
[Termes IGN] test de performanceRésumé : (auteur) This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a network from scratch for each image to be segmented. We propose a convolutional encoder–decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers’ statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image boosts the network performance with minimal human intervention. We evaluate our architecture over three data sets of satellite images, showing the state-of-the-art performance in binary segmentation of previously unseen images and competitive performance with respect to more complex techniques in a multiclass segmentation task. Numéro de notice : A2019-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906689 Date de publication en ligne : 17/04/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2906689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93379
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6517 - 6529[article]The Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment / Michele Manunta in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
[article]
Titre : The Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment Type de document : Article/Communication Auteurs : Michele Manunta, Auteur ; Claudio De Luca, Auteur ; Ivana Zinno, Auteur ; Francesco Casu, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 6259 - 6281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] chaîne de traitement
[Termes IGN] données GPS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] interferométrie différentielle
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Italie
[Termes IGN] qualité du processus
[Termes IGN] série temporelleRésumé : (auteur) We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015–April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h. The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale. Numéro de notice : A2019-624 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2904912 Date de publication en ligne : 24/05/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2904912 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93374
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6259 - 6281[article]Time-lapse photogrammetry of distributed snow depth during snowmelt / Simon Filhol in Water resources research, vol 55 n° 9 (September 2019)
[article]
Titre : Time-lapse photogrammetry of distributed snow depth during snowmelt Type de document : Article/Communication Auteurs : Simon Filhol, Auteur ; Alexis Perret , Auteur ; Luc Girod , Auteur ; Guillaume Sutter, Auteur ; Thomas V. Schuler, Auteur ; John F. Burkhart, Auteur Année de publication : 2019 Projets : 3-projet - voir note / Note générale : bibliographie
This work was supported by the Norwegian Research Council—Enhancing Snow Competency of Models and Operators(ESCYMO) project (NFR 244024), the University of Oslo eInfrastructure Competence Hub Geohive, and the European Research Council under the European Union's Seventh FrameworkProgram (FP/2007-2013)/ERC GrantAgreement 320816. This work forms a contribution to LATICE, which is a strategic research initiative funded by the Faculty of Mathematics and Natural Sciences at the University of Oslo. The source code of the software is available at this site (https://github.com/ArcticSnow/photo4D). Data are freely available from Zenodo.org (Filhol et al., 2018).FILHOL ET AL.7925Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] chaîne de traitement
[Termes IGN] code source libre
[Termes IGN] eau de fonte
[Termes IGN] manteau neigeux
[Termes IGN] Norvège
[Termes IGN] profondeur
[Termes IGN] semis de pointsRésumé : (auteur) Characterizing snowmelt both spatially and temporally from in situ observation remains a challenge. Available sensors (i.e., sonic ranger, lidar, airborne photogrammetry) provide either time series of local point measurements or sporadic surveys covering larger areas. We propose a methodology to recover from a minimum of three synchronized time-lapse cameras changes in snow depth and snow cover extent over area smaller or equivalent to 0.12 km2. Our method uses photogrammetry to compute point clouds from a set of three or more images and automatically repeat this task for the entire time series. The challenges were (1) finding an optimal experimental setup deployable in the field, (2) estimating the error associated with this technique, and (3) being able to minimize the input of manual work in the data processing pipeline. Developed and tested in the field in Finse, Norway, over 1 month during the 2018 melt season, we estimated a median melt of 2.12 ± 0.48 m derived from three cameras 1.2 km away from the region of interest. The closest weather station recorded 1.94 m of melt. Other parameters like snow cover extent and duration could be estimated over a 300 × 400m region. The software is open source and applicable to a broader range of geomorphologic processes like glacier dynamic, snow accumulation, or any other processes of surface deformation, with the conditions of (1) having fixed visible points within the area of interest and (2) resolving sufficient surface textures in the photographs. Numéro de notice : A2019-663 Affiliation des auteurs : ENSG+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2018WR024530 Date de publication en ligne : 19/08/2019 En ligne : https://doi.org/10.1029/2018WR024530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99691
in Water resources research > vol 55 n° 9 (September 2019)[article]Validating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. 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