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Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)
[article]
Titre : Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data Type de document : Article/Communication Auteurs : Yueting Wang, Auteur ; Xiaoli Zhang, Auteur ; Zhengqi Guo, Auteur Année de publication : 2021 Article en page(s) : n° 107645 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] forêt
[Termes IGN] hauteur des arbres
[Termes IGN] image Sentinel-MSI
[Termes IGN] image ZiYuan-3
[Termes IGN] modèle numérique de surface
[Termes IGN] Pinophyta
[Termes IGN] surveillance forestièreRésumé : (auteur) The forest tree height and aboveground biomass (AGB) are important indicators for monitoring changes and trends in forest carbon storage and terrestrial carbon fluxes. Accurate large-scale wall-to-wall mapping of the forest tree height and AGB remain challenging due to the limited data availability for extraction tree height and the data signal saturation problem in AGB estimation. In this study, we explored the potential of forest tree height mapping using stereo imageries, and analyzed whether accounting for such information, in addition to optical sensor data, could improve the performance of AGB estimations of coniferous forests in a case study in North China. First, a spatially continuous tree height product was obtained using Ziyuan-3 satellite (ZY-3) stereo images combined with a digital elevation model (DEM) obtained from Advanced Land Observing Satellite (ALOS) data. Second, two AGB estimation models were established by combining the forest tree height with vegetation index, spectral, biophysical (from Sentinel-2 images), and topographic variables. A random forest algorithm was utilized to evaluate the effect of including the tree height variable in the AGB estimation. The results showed that the tree height estimation using the nadir and forward views of the ZY-3 stereo images was more accurate than that based on the nadir and backward views from the same images. The AGB estimation model incorporating the tree height variable with a coefficient of determination value of 0.7789, a root mean square error (RMSE) value of 29.815 Mg/ha and a relative RMSE of 23.42% was more robust and effective, thereby demonstrating that the tree height variable can be used to alleviate the data signal saturation issue successfully. The proposed approach can provide new insight into forest tree height mapping and AGB products obtained from satellite stereo images and freely accessible Sentinel-2 multispectral images. Numéro de notice : A2021-942 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ecolind.2021.107645 En ligne : https://doi.org/10.1016/j.ecolind.2021.107645 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99753
in Ecological indicators > vol 126 (July 2021) . - n° 107645[article]Multisensor data fusion for cloud removal in global and all-season Sentinel-2 imagery / Patrick Ebel in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Multisensor data fusion for cloud removal in global and all-season Sentinel-2 imagery Type de document : Article/Communication Auteurs : Patrick Ebel, Auteur ; Andrea Meraner, Auteur ; Michael Schmitt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5866 - 5878 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection des nuages
[Termes IGN] données multicapteurs
[Termes IGN] image Sentinel-MSI
[Termes IGN] nuage
[Termes IGN] reconstruction d'image
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) The majority of optical observations acquired via spaceborne Earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are, oftentimes, confined to narrowly defined regions of interest, raising the question of whether an approach can generalize to a diverse set of observations acquired at variable cloud coverage or in different regions and seasons. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate two recently proposed performance metrics of image quality and diversity. Our data set is the first publically available to contain a global sample of coregistered radar and optical observations, cloudy and cloud-free. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extreme and evaluate its performance on our proposed data set. Finally, we demonstrate the superiority of training models on real over synthetic data, underlining the need for a carefully curated data set of real observations. To facilitate future research, our data set is made available online. Numéro de notice : A2021-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3024744 Date de publication en ligne : 02/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3024744 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97980
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5866 - 5878[article]Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space / Min Wu in The Visual Computer, vol 37 n° 7 (July 2021)
[article]
Titre : Remote sensing image colorization using symmetrical multi-scale DCGAN in YUV color space Type de document : Article/Communication Auteurs : Min Wu, Auteur ; Xin Jin, Auteur ; Qian Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1707 - 1729 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contraste de couleurs
[Termes IGN] données multiéchelles
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] niveau de gris (image)
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Image colorization technique is used to colorize the gray-level image or single-channel image, which is a very significant and challenging task in image processing, especially the colorization of remote sensing images. This paper proposes a new method for coloring remote sensing images based on deep convolution generation adversarial network. The adopted generator model is a symmetrical structure using the principle of auto-encoder, and a multi-scale convolutional module is specially designed to introduce into the generator model. Thus, the proposed generator can enable the whole model to retain more image features in the process of up-sampling and down-sampling. Meanwhile, the discriminator uses residual neural network 18 that can compete with the generator, so that the generator and discriminator can effectively optimize each other. In the proposed method, the color space transformation technique is first utilized to convert remote sensing images from RGB to YUV. Then, the Y channel (a gray-level image) is used as the input of the neural network model to predict UV channels. Finally, the predicted UV channels are concatenated with the original Y channel as a whole YUV that is then transformed into RGB space to get the final color image. Experiments are conducted to test the performance of different image colorization methods, and the results show that the proposed method has good performance in both visual quality and objective indexes on the colorization of remote sensing image. Numéro de notice : A2021-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01933-2 Date de publication en ligne : 28/08/2020 En ligne : https://doi.org/10.1007/s00371-020-01933-2 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98018
in The Visual Computer > vol 37 n° 7 (July 2021) . - pp 1707 - 1729[article]Review of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)
[article]
Titre : Review of spectral indices for urban remote sensing Type de document : Article/Communication Auteurs : Akib Javed, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 513 - 524 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande spectrale
[Termes IGN] classification non dirigée
[Termes IGN] détection du bâti
[Termes IGN] indice de détection
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] surface imperméableRésumé : (Auteur) Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available spectral bands, and their merits and demerits. Numéro de notice : A2021-572 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.513 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.14358/PERS.87.7.513 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98167
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 7 (July 2021) . - pp 513 - 524[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021071 SL Revue Centre de documentation Revues en salle Disponible Road-network-based fast geolocalization / Yongfei Li in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Road-network-based fast geolocalization Type de document : Article/Communication Auteurs : Yongfei Li, Auteur ; Dongfang Yang, Auteur ; Shisheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6065 - 6076 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carrefour
[Termes IGN] carte routière
[Termes IGN] cohérence géométrique
[Termes IGN] géolocalisation
[Termes IGN] image aérienne
[Termes IGN] réseau routier
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] superposition d'images
[Termes IGN] transformation homographique
[Termes IGN] zone urbaineRésumé : (auteur) In this article, a road-network-based geolocalization method is proposed. We match roads in the onboard images to the reference road vector map, and realize successful localization over areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under the homography transformation and solved under the hypothesize-and-test framework. To tackle the point cloud registration problem, a global projective-invariant feature is proposed, which consists of two road intersections augmented with their tangents. In addition, we propose the necessary conditions for the features to match. This can reduce the candidate matching features, thus accelerating the search to a great extent. These matching candidates are first “filtered” with the model consistency check in parameter space and then tested with similarity metrics to identify the correct transformation. The experiments show that our method can localize an aerial image over an area larger than 1000 km 2 within several seconds on a single CPU. Our code can be found at: https://github.com/FlyAlCode/RCLGeolocalization-2.0 . Numéro de notice : A2021-532 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011034 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97989
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6065 - 6076[article]Semantic-aware label placement for augmented reality in street view / Jianqing Jia in The Visual Computer, vol 37 n° 7 (July 2021)PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkSpatio-temporal-spectral observation model for urban remote sensing / Zhenfeng Shao in Geo-spatial Information Science, vol 24 n° 3 (July 2021)PermalinkMarrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkAn automatic workflow for orientation of historical images with large radiometric and geometric differences / Ferdinand Maiwald in Photogrammetric record, vol 36 n° 174 (June 2021)PermalinkAn incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkApplication of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkDomain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkEfficient image dataset classification difficulty estimation for predicting deep-learning accuracy / Florian Scheidegger in The Visual Computer, vol 37 n° 6 (June 2021)Permalink