Descripteur
Termes IGN > imagerie > image numérique > image optique > image multibande
image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
Documents disponibles dans cette catégorie (950)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Titre : Télédétection et modélisation spatiale : Applications à la surveillance et au contrôle des maladies liées aux moustiques Type de document : Monographie Auteurs : Annelise Tran, Éditeur scientifique ; Eric Daudé, Éditeur scientifique ; Thibault Catry, Éditeur scientifique Editeur : Versailles : Quae Année de publication : 2022 Importance : 148 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-2-7592-3629-9 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse multicritère
[Termes IGN] cartographie des risques
[Termes IGN] distribution spatiale
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-ETM+
[Termes IGN] image multibande
[Termes IGN] image Terra-MODIS
[Termes IGN] maladie parasitaire
[Termes IGN] maladie tropicale
[Termes IGN] modélisation spatiale
[Termes IGN] Normalized Difference Water Index
[Termes IGN] surveillance sanitaire
[Termes IGN] température de l'air
[Termes IGN] TRMMRésumé : (éditeur) Mosquitoes are vectors of many disease-causing agents, such as malaria, dengue, chikungunya and yellow fever. According to the World Health Organisation, they cause several hundred thousand deaths each year. They are also the cause of zoonoses, such as Rift Valley fever and West Nile fever. In this context, there is a great need for operational tools to guide surveillance and control actions, both in the South - tropical and subtropical areas are the most affected by mosquito-borne diseases - and in the North, where the establishment of new species such as the tiger mosquito increases the risk of disease emergence. Earth observation imagery is of great interest to meet these needs: the spatial distribution and temporal dynamics of mosquitoes are influenced by climatic (temperature, precipitation, humidity) and environmental (availability of water areas, vegetation) variables, indicators of which can be derived from satellite imagery. Many recent studies have developed innovative methods combining remote sensing and spatial modelling to predict the spatial and temporal dynamics of mosquito vectors and associated diseases. Beyond the feasibility study, some of these methods have led to tools and processing chains that are now operational and used by public health actors and vector control operators. This book, intended for students and researchers as well as public health actors, presents a summary of this research work and these tools. Note de contenu : Introduction générale
Partie I- Informations spatiales pour la surveillance des moustiques vecteurs et des maladies associées
1- Liens entre moustiques vecteurs et environnement : apport des méthodes de télédétection satellite
2- Indices spectraux et classifications d’images multispectrales pour la cartographie du risque vectoriel
3- Estimation des températures de l’air à partir d’images satellite et de stations météorologiques
4- Du recensement au bâtiment : génération de populations synthétiques
5- Texture des images satellite et caractérisation des milieux urbains favorables aux moustiques vecteurs
Partie II- Analyser et prédire l’effet de variables environnementales sur la distribution et la dynamique des moustiques vecteurs
6- Modèles basés sur les données : cartographier la distribution spatiale des vecteurs
7- Modèles fondés sur les connaissances : exemple d’un outil d’évaluation multicritère pour la santé publique
8- Arbocarto : un modèle mécaniste fondé sur le cycle de vie des moustiques Aedes
9- Simulation spatiale du risque de propagation de la dengue à partir de modèles comportementaux vecteurs et hôtes
Conclusion générale et perspectivesNuméro de notice : 24096 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.35690/978-2-7592-3629-9 En ligne : https://doi.org/10.35690/978-2-7592-3629-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102570 Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)
[article]
Titre : Early detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany Type de document : Article/Communication Auteurs : Kathrin Einzmann, Auteur ; Clement Atzberger, Auteur ; Nicole Pinnel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112676 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Bavière (Allemagne)
[Termes IGN] changement climatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dépérissement
[Termes IGN] détection de changement
[Termes IGN] houppier
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] insecte nuisible
[Termes IGN] phénomène climatique extrême
[Termes IGN] Picea abies
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelle
[Termes IGN] stress hydriqueRésumé : (auteur) Vitality loss of trees caused by extreme weather conditions, drought stress or insect infestations, are expected to increase with ongoing climate change. The detection of vitality loss at an early stage is thus of vital importance for forestry and forest management to minimize ecological and economical damage. Remote sensing instruments are able to detect changes over large areas down to the level of individual trees. The scope of our study is to investigate whether it is possible to detect stress-related spectral changes at an early stage using hyperspectral sensors. For this purpose, two Norway spruce (Picea abies) forest stands, both different in age and maintenance, were monitored in the field over two vegetation periods. In parallel, time series of airborne hyperspectral remote sensing data were acquired. For each stand 70 trees were artificially stressed (ring-barked) and 70 trees were used as control trees. The data collected in south-eastern Germany consists of measurements at multiple times and at different scales: (1) crown conditions were visually assessed in the field (2) needle reflectance spectra were acquired in the laboratory using a FieldSpec spectrometer, and (3) hyperspectral airborne data (HySpex) were flown at 0.5 m spatial resolution. We aimed for a simultaneous data acquisition at the three levels. This unique data set was investigated whether any feature can be discriminated to detect vitality loss in trees at an early stage. Several spectral transformations were applied to the needle and tree crown spectra, such as spectral derivatives, vegetation indices and angle indices. All features were examined for their separability (ring-barked vs. control trees) with the Random Forest (RF) classification algorithm. As result, the younger, well maintained forest stand only showed minor changes over the 2-year period, whereas changes in the older forest stand were observable both in the needle and in the hyperspectral tree crown spectra, respectively. These changes could even be detected before changes were visible by field observations. The tree spectral reactions to ring-barking were first noticeable 11 months after ring-barking and 6 weeks before they were visible by field inspection. The most discriminative features for separating the two groups were the reflectance spectra and the spectral derivatives, over the VIs or angle indices. The tree crown spectra of the two groups could be separated by the RF classifier with a 79% overall accuracy at the beginning of the second vegetation period and 1 month later with 92% overall accuracy with high kappa index. The results clearly demonstrate the great potential of hyperspectral remote sensing in detecting early vitality changes of stressed trees. Numéro de notice : A2021-921 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112676 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112676 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99274
in Remote sensing of environment > vol 266 (December 2021) . - n° 112676[article]Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : Flexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Qingqing Zhao, Auteur ; Jiayue Zhuang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 10394 - 10409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] ondelette de Gabor
[Termes IGN] segmentation d'image
[Termes IGN] superpixelRésumé : (auteur) Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG- Su ULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial–spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called Su ULDA, is skillfully introduced to reduce the extracted large amount of FG features. The Su ULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG- Su ULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG- Su ULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility. Numéro de notice : A2021-872 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3048994 Date de publication en ligne : 20/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3048994 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99131
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10394 - 10409[article]Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)
[article]
Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande infrarouge
[Termes IGN] coefficient de corrélation
[Termes IGN] couvert forestier
[Termes IGN] détection de contours
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle statistique
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] régression
[Termes IGN] surveillance de la végétationRésumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318
in Forests > vol 12 n° 12 (December 2021) . - n° 1768[article]Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation Type de document : Article/Communication Auteurs : Hamid Jafarzadeh, Auteur ; Masoud Mahdianpari, Auteur ; Eric Gill, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] ensachage
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image ROSISRésumé : (auteur) In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Numéro de notice : A2021-823 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214405 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.3390/rs13214405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98938
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4405[article]Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkAdaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkEarly detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (October-1 2021)PermalinkEndmember bundle extraction based on multiobjective optimization / Rong Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkField scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkIntegrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkSpectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)PermalinkClassification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkDetection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)PermalinkHyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkLarge-area inventory of species composition using airborne laser scanning and hyperspectral data / Hans Ole Ørka in Silva fennica, vol 55 n° 4 (September 2021)PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkDetail injection-based deep convolutional neural networks for pansharpening / Liang-Jian Deng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkTarget-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkForest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 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])PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkMultiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkPerformance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (May 2021)PermalinkUnsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkAnti-cross validation technique for constructing and boosting random subspace neural network ensembles for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 6 ([01/04/2021])PermalinkAutomatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkUnsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkApplication of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkDetection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction / Xiaorui Song in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkImproving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])PermalinkPan-sharpening via multiscale dynamic convolutional neural network / Jianwen Hu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkCorrentropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkFully convolutional neural network for impervious surface segmentation in mixed urban environment / Joseph McGlinchy in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)PermalinkGTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkSpruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)Permalink3D urban scene understanding by analysis of LiDAR, color and hyperspectral data / David Duque-Arias (2021)PermalinkAmélioration des résolutions spatiale et spectrale d’images satellitaires par réseaux antagonistes / Anaïs Gastineau (2021)PermalinkApport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)PermalinkApport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (2021)PermalinkPermalinkAutomated detection of individual Juniper tree location and forest cover changes using Google Earth Engine / Sudeera Wickramarathna in Annals of forest research, vol 64 n° 1 (2021)PermalinkHyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition / Yuanyang Bu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkModel based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)PermalinkPermalink