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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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CSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
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Titre : CSVM architectures for pixel-wise object detection in high-resolution remote sensing images Type de document : Article/Communication Auteurs : Youyou Li, Auteur ; Farid Melgani, Auteur ; Binbin He, Auteur Année de publication : 2020 Article en page(s) : pp 6059 - 6070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'objet
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] processeur graphiqueRésumé : (auteur) Detecting objects becomes an increasingly important task in very high resolution (VHR) remote sensing imagery analysis. With the development of GPU-computing capability, a growing number of deep convolutional neural networks (CNNs) have been designed to address the object detection challenge. However, compared with CPU, GPU is much more costly. Therefore, GPU-based methods are less attractive in practical applications. In this article, we propose a CPU-based method that is based on convolutional support vector machines (CSVMs) to address the object detection challenge in VHR images. Experiments are conducted on three VHR and two unmanned aerial vehicle (UAV) data sets with very limited training data. Results show that the proposed CSVM achieves competitive performance compared to U-Net which is an efficient CNN-based model designed for small training data sets. Numéro de notice : A2020-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2972289 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2972289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95705
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6059 - 6070[article]Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
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Titre : Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 Type de document : Article/Communication Auteurs : Helena Bergstedt, Auteur ; Annett Bartsch, Auteur ; Anton Neureiter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6008 - 6019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Autriche
[Termes IGN] bande C
[Termes IGN] courbe de Pearson
[Termes IGN] dégel
[Termes IGN] Finlande
[Termes IGN] fonte des glaces
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image MetOp-ASCAT
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] pergélisol
[Termes IGN] rétrodiffusion
[Termes IGN] série temporelle
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] température au solRésumé : (auteur) Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25–50 km). Current approaches do not adequately resolve spatial heterogeneity in landscape-scale freeze–thaw processes. We propose to derive a frozen fraction instead of binary freeze–thaw information. This introduces the possibility to monitor the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 synthetic aperture radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of −0.85 to −0.96) for the sites in northern Finland and less strong relationships for the Alpine site (Pearson correlations −0.579 and −0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. The validation of the Sentinel-1 derived freeze–thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%–94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape-scale surface state. Numéro de notice : A2020-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2967364 Date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2967364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95702
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6008 - 6019[article]Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles / Re-Yang Lee in Geocarto international, vol 35 n° 12 ([01/09/2020])
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Titre : Evaluation of crop mapping on fragmented and complex slope farmlands through random forest and object-oriented analysis using unmanned aerial vehicles Type de document : Article/Communication Auteurs : Re-Yang Lee, Auteur ; Kuo-Chen Chang, Auteur ; Deng-Yuan Ou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1293 - 1310 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image captée par drone
[Termes IGN] interprétation automatique
[Termes IGN] pente
[Termes IGN] TaïwanRésumé : (auteur) Conducting field research in Taiwan can be challenging because of the abundance of steep slopes. This study aimed to establish an automatic interpretation procedure applicable to exploring images of large-scale slope land taken using UAVs. The proposed method was compared with traditional field surveying and manual image interpretation techniques to determine the advantages and disadvantages of the proposed procedure in terms of efficiency. The object-based image analysis (OBIA) and texture features were first combined and the random forest (RF) classifier was then employed to interpret crop types. This study selected three sites of slope land and plains for experimentation. The obtained results indicated that the overall accuracy of the proposed classification method exceeded 91%, and the Kappa value was approximately 0.9 for all sites. In addition, interpretation of the proposed method was more efficient than that of the two traditional methods. Numéro de notice : A2020-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559886 Date de publication en ligne : 04/06/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559886 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95628
in Geocarto international > vol 35 n° 12 [01/09/2020] . - pp 1293 - 1310[article]Heliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
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Titre : Heliport detection using artificial neural networks Type de document : Article/Communication Auteurs : Emre Baseski, Auteur Année de publication : 2020 Article en page(s) : pp 541-546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] hélicoptère
[Termes IGN] image à haute résolution
[Termes IGN] réseau neuronal artificiel
[Termes IGN] zone militaireRésumé : (Auteur) Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques. Numéro de notice : A2020-439 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.9.541 Date de publication en ligne : 01/09/2020 En ligne : https://doi.org/10.14358/PERS.86.9.541 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95929
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 9 (September 2020) . - pp 541-546[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2020091 SL Revue Centre de documentation Revues en salle Disponible Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)
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Titre : Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation Type de document : Article/Communication Auteurs : Muhamad Farid Ramli, Auteur ; Khairul Nizam Tahar, Auteur Année de publication : 2020 Article en page(s) : pp 195 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] Arecaceae
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] image captée par drone
[Termes IGN] Malaisie
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] pas d'échantillonnage au sol
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] QGIS
[Termes IGN] SAGA GIS
[Termes IGN] segmentation en régionsRésumé : (auteur) The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task. Numéro de notice : A2020-562 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805366 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805366 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95878
in Geo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 195 - 208[article]Hyperspectral unmixing using orthogonal sparse prior-based autoencoder with hyper-laplacian loss and data-driven outlier detection / Zeyang Dou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkIlluminating the spatio-temporal evolution of the 2008–2009 Qaidam earthquake sequence with the joint use of Insar time series and teleseismic data / Simon Daout in Remote sensing, vol 12 n° 17 (September-1 2020)
PermalinkLocal color and morphological image feature based vegetation identification and its application to human environment street view vegetation mapping, or how green is our county? / Istvan G. Lauko in Geo-spatial Information Science, vol 23 n° 3 (September 2020)
PermalinkMapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkMapping quality prediction for RTK/PPK-equipped micro-drones operating in complex natural environment / Emmanuel Clédat in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkMonitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)
PermalinkMultiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkA novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkA novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkPansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkPrecise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkSemi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkShip detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)
PermalinkVehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkShoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)
PermalinkAccuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkCan ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkCan SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkCNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkConjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data / Bingquan Li in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)
PermalinkCorrection of systematic radiometric inhomogeneity in scanned aerial campaigns using principal component analysis / Lâmân Lelégard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkDevelopment and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkEstimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
PermalinkExtraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkGeometric distortion of historical images for 3D visualization / Evelyn Paiz-Reyes in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkGuided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkLanduse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])
PermalinkLeveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkOn-Orbit Calibration of Terra MODIS VIS Bands Using Polarization-Corrected Desert Observations / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
PermalinkPredicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)
PermalinkRecent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkTowards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkA worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkCartographie des surfaces pastorales à l’aide des données Sentinel 2 L3A et des données ouvertes : Promesses et réalités / Urcel Kalenga Tshingomba in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)
PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)
PermalinkComplete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
PermalinkCross-calibration of MODIS reflective solar bands with Sentinel 2A/2B MSI instruments / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)
PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)
PermalinkMapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkSemi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)
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