Catalogue en ligne IGN

>> Accueil > Bulletin Imagerie

Nouvelle recherche

Bulletin Imagerie

Bulletin-imagerie

Ten years of TerraSAR-X : scientific results / Michael Eineder (2019)

Public

Titre :	Ten years of TerraSAR-X : scientific results
Type de document :	Monographie
Auteurs :	Michael Eineder, Editeur scientifique ; Achim Roth, Editeur scientifique ; Alberto Moreira, Editeur scientifique
Editeur :	Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI
Année de publication :	2019
Importance :	422 p.
ISBN/ISSN/EAN :	978-3-03897-725-4
Note générale :	This book is a printed edition of the Special Issue Ten Years of TerraSAR-X—Scientific Results that was published in the open journal "Remote Sensing" from 2018 to 2019.
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Télédétection [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] MNS TerraSAR & TanDEM-X [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] télédétection en hyperfréquence [Termes descripteurs IGN] TerraSAR-X
Résumé :	(Editeur) This Special Issue is a collection of papers addressing the scientific use of data acquired in the course of the TerraSAR-X mission 10 years after launch. The articles deal with the mission itself, the accuracy of the products, with differential interferometry, and with applications in the domains cryosphere, oceans, wetlands, and urban areas.
Numéro de notice :	26042
Thématique :	IMAGERIE
Nature :	Monographie
DOI :	10.3390/books978-3-03897-725-4
date de publication en ligne :	25/04/2019
En ligne :	https://doi.org/10.3390/books978-3-03897-725-4
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=93611

An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 50 - 62

Titre :	An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data
Type de document :	Article/Communication
Auteurs :	Puzhao Zhang, Auteur ; Andrea Nascetti, Auteur ; Yifang Ban, Auteur ; Maoguo Gong, Auteur
Année de publication :	2019
Article en page(s) :	pp 50 - 62
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] incendie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Short Waves InfraRed
Résumé :	(auteur) Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multitemporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively.
Numéro de notice :	A2019-545
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.09.013
date de publication en ligne :	04/10/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.09.013
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=94189

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019121	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019123	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019)

Titre :	Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture
Type de document :	Article/Communication
Auteurs :	Dino Lenco, Auteur ; Roberto Interdonato, Auteur ; Raffaele Gaetano, Auteur ; Ho Tong Minh Dinh, Auteur
Année de publication :	2019
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Burkina Faso [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Réunion, île de la [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] utilisation du sol
Résumé :	(auteur) The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal.
Numéro de notice :	A2019-544
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.09.016
date de publication en ligne :	27/09/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.09.016
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=94186

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019121	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019123	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Innovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)

Public

[article]
inApplied geomatics > Vol 11 n° 4 (December 2019) . - pp 353–369

Titre :	Innovative techniques of photogrammetry for 3D modeling
Type de document :	Article/Communication
Auteurs :	Vicenzo Barrile, Auteur ; Alice Pozzoli, Auteur ; Giuliana Bilotta, Auteur ; Antonino Fotia, Auteur
Année de publication :	2019
Article en page(s) :	pp 353–369
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Photogrammétrie analytique [Termes descripteurs IGN] Italie [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] orientation absolue [Termes descripteurs IGN] orientation automatique [Termes descripteurs IGN] orientation relative [Termes descripteurs IGN] Raspberry Pi [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] structure-from-motion [Termes descripteurs IGN] vision par ordinateur
Résumé :	(auteur) This note presents the experimental results deriving from the application of two innovative photogrammetric techniques (with particular reference to non-conventional photogrammetric applications) for the production of time-space 3D models of the marine surface. Moreover, the first method (automatic three images processing (ATIP)) proposes some easy procedures to solve typical non-linear problems of analytical photogrammetry. In particular, once validated the technique of orientation of two images (two-step procedure based on two phases: relative orientation and absolute orientation, both characterized by non-linear functions), we propose a procedure for the automatic orientation of three images (the introduction of a third image allows avoiding human decision to find the final solution). The second method (Computer Vision Raspberry Pi—CVR) refers to the use of the “prompt” technique of computer vision (structure from motion) using five appropriately synchronized cameras to acquire simultaneously the various frames, thanks to the use of an acquisition system based on the use of Raspberry Pi. The experimentation was conducted both in the laboratory (on a model that allows to study a typical phenomenon of the Alpine Valtellina region, in the North of Italy) that directly at sea (on a portion of marine surface located in Reggio Calabria near the seafront). The results obtained show a substantial comparability of the results both between the two methods and with the actual data measured at sea with dedicated instrumentation.
Numéro de notice :	A2019-533
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1007/s12518-019-00264-9
date de publication en ligne :	22/05/2019
En ligne :	https://doi.org/10.1007/s12518-019-00264-9
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=94126

[article]

Matching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 241 - 248

Titre :	Matching of TerraSAR-X derived ground control points to optical image patches using deep learning
Type de document :	Article/Communication
Auteurs :	Tatjana Bürgmann, Auteur ; Wolfgang Koppe, Auteur ; Michael Schmitt, Auteur
Année de publication :	2019
Article en page(s) :	pp 241 - 248
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Pléiades [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] point d'appui
Résumé :	(auteur) High resolution synthetic aperture radar (SAR) satellites like TerraSAR-X are capable of acquiring images exhibiting an absolute geolocation accuracy within a few centimeters, mainly because of the availability of precise orbit information and by compensating range delay errors due to atmospheric conditions. In contrast, satellite images from optical missions generally exhibit comparably low geolocation accuracies because of the propagation of errors in angular measurements over large distances. However, a variety of remote sensing applications, such as change detection, surface movement monitoring or ice flow measurements, require precisely geo-referenced and co-registered satellite images. By using Ground Control Points (GCPs) derived from TerraSAR-X, the absolute geolocation accuracy of optical satellite images can be improved. For this purpose, the corresponding matching points in the optical images need to be localized. In this paper, a deep learning based approach is investigated for an automated matching of SAR-derived GCPs to optical image elements. Therefore, a convolutional neural network is pretrained with medium resolution Sentinel-1 and Sentinel-2 imagery and fine-tuned on precisely co-registered TerraSAR-X and Pléiades training image pairs to learn a common descriptor representation. By using these descriptors, the similarity of SAR and optical image patches can be calculated. This similarity metric is then used in a sliding window approach to identify the matching points in the optical reference image. Subsequently, the derived points can be utilized for co-registration of the underlying images. The network is evaluated over nine study areas showing airports and their rural surroundings from several different countries around the world. The results show that based on TerraSAR-X-derived GCPs, corresponding points in the optical image can automatically and reliably be identified with a pixel-level localization accuracy.
Numéro de notice :	A2019-548
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.09.010
date de publication en ligne :	05/11/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.09.010
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=94194

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019121	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019123	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

On the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 113 - 122

Titre :	On the value of corner reflectors and surface models in InSAR precise point positioning
Type de document :	Article/Communication
Auteurs :	Mengshi Yang, Auteur ; Paco Lopez-Dekker, Auteur ; Prabu Dheenathayalan, Auteur ; Mingsheng Liao, Auteur ; Ramon F. Hanssen, Auteur
Année de publication :	2019
Article en page(s) :	pp 113 - 122
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] correction d'image [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] MNS lidar [Termes descripteurs IGN] Pays-Bas [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] positionnement ponctuel précis
Résumé :	(auteur) To correctly interpret the estimated displacements in InSAR point clouds, especially in the built environment, these need to be linked to real-world structures. This requires the accurate and precise 3D positioning of each point. Artificial ground control points (GCPs), such as corner reflectors, serve this purpose, but since they require efforts and resources, there is a need for criteria to assess their usefulness. Here we evaluate the value and necessity of using GCPs for different scenarios, concerning the required efforts, and compare this to alternatives such as digital surface models (DSM) and advanced (geo) physical corrections. We consider single-epoch as well as multi-epoch GCP deployment, reflect on the number of GCPs required in relation to the number of SAR data acquisitions, and compare this with digital surface models of different quality levels. Analyzing the geolocation performance using TerraSAR-X and Sentinel-1 data, we evaluate the pros and cons of various deployment options and show that the multi-epoch deployment of a GCP yields optimal geolocalization results in terms of precision, accuracy, and reliability.
Numéro de notice :	A2019-546
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.10.006
date de publication en ligne :	25/10/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.10.006
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=94191

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019121	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019123	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 108 - 123

Titre :	Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits
Type de document :	Article/Communication
Auteurs :	Tawanda W. Gara, Auteur ; Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; et al., Auteur
Année de publication :	2019
Article en page(s) :	pp 108 - 123
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] Leaf mass per area [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] variation saisonnière
Résumé :	(Auteur) Leaf traits at canopy level (hereinafter canopy traits) are conventionally expressed as a product of total canopy leaf area index (LAI) and leaf trait content based on samples collected from the exposed upper canopy. This traditional expression is centered on the theory that absorption of incident photosynthetically active radiation (PAR) follow a bell-shaped function skewed to the upper canopy. However, the validity of this theory has remained untested for a suite of canopy traits in a temperate forest ecosystem across multiple seasons using multispectral imagery. In this study, we examined the effect of canopy traits expression in modelling canopy traits using Sentinel-2 multispectral data across the growing season in Bavaria Forest National Park (BFNP), Germany. To achieve this, we measured leaf mass per area (LMA), chlorophyll (Cab), nitrogen (N) and carbon content and LAI from the exposed upper and shaded lower canopy respectively over three seasons (spring, summer and autumn). Subsequently, we estimated canopy traits using two expressions, i.e. the traditional expression-based on the product of LAI and leaf traits content of samples collected from the sunlit upper canopy (hereinafter top-of-canopy expression) and the weighted expression - established on the proportion between the shaded lower and sunlit upper canopy LAI and their respective leaf traits content. Using a Random Forest machine-learning algorithm, we separately modelled canopy traits estimated from the two expressions using Sentinel-2 spectral bands and vegetation indices. Our results showed that dry matter related canopy traits (LMA, N and carbon) estimated based on the weighted canopy expression yield stronger correlations and higher prediction accuracy (NRMSECV  0.48 µg/cm2) across all seasons. We also developed a generalized model that explained 52.57–67.82% variation in canopy traits across the three seasons. Using the most accurate Random Forest model for each season, we demonstrated the capability of Sentinel-2 data to map seasonal dynamics of canopy traits across the park. Results presented in this study revealed that canopy trait expression can have a profound effect on modelling the accuracy of canopy traits using satellite imagery throughout the growing seasons. These findings have implications on model accuracy when monitoring the dynamics of ecosystem functions, processes and services.
Numéro de notice :	A2019-493
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.09.005
date de publication en ligne :	11/09/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.09.005
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=93725

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019111	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019113	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 155 - 170

Titre :	Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
Type de document :	Article/Communication
Auteurs :	Cheolhee Yoo, Auteur ; Daehyeon Han, Auteur ; Jungho Im, Auteur ; Benjamin Bechtel, Auteur
Année de publication :	2019
Article en page(s) :	pp 155 - 170
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chicago (Illinois) [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Madrid (Espagne) [Termes descripteurs IGN] Rome [Termes descripteurs IGN] World Urban Database and Access Portal Tools [Termes descripteurs IGN] zone urbaine dense
Résumé :	(Auteur) The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning.
Numéro de notice :	A2019-495
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.09.009
date de publication en ligne :	19/09/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.09.009
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=93728

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019111	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019113	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215

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 :	[Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gradient [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique
Résumé :	(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
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

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019111	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019113	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)

Public

[article]
inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 124 - 136

Titre :	Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery
Type de document :	Article/Communication
Auteurs :	Yuri Shendryk, Auteur ; Yannik Rist, Auteur ; Catherine Ticehurst, Auteur ; Peter Thorburn, Auteur
Année de publication :	2019
Article en page(s) :	pp 124 - 136
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Australie [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'ombre [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] nuage [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] zone tropicale humide
Résumé :	(Auteur) With the increasing availability of high-resolution satellite imagery it is important to improve the efficiency and accuracy of satellite image indexing, retrieval and classification. Furthermore, there is a need for utilizing all available satellite imagery in identifying general land cover types and monitoring their changes through time irrespective of their spatial, spectral, temporal and radiometric resolutions. Therefore, in this study, we developed deep learning models able to efficiently and accurately classify cloud, shadow and land cover scenes in different high-resolution (
Numéro de notice :	A2019-494
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2019.08.018
date de publication en ligne :	17/09/2019
En ligne :	https://doi.org/10.1016/j.isprsjprs.2019.08.018
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=93727

[article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019111	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2019113	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2019112	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Voir la suite...

IGN

Centre dedocumentation
scientifique

Accueil

Sélection de la langue

Adresse

Se connecter

Actualité

L'actu-43

Agenda

Informations pratiques

Bulletin Imagerie

Bulletin-imagerie

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

IGN

L'IGN a pour vocation

Accès directs

2014 IGN

IGN

Centre dedocumentationscientifique

Accueil

Sélection de la langue

Adresse

Se connecter

Actualité

L'actu-43

Agenda

Informations pratiques

Bulletin Imagerie

Bulletin-imagerie

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

Réservation

Exemplaires (3)

IGN

L'IGN a pour vocation

Accès directs

2014 IGN

Centre dedocumentation
scientifique