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Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
[article]
Titre : Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints Type de document : Article/Communication Auteurs : Li Liu, Auteur ; Luping Xu, Auteur ; Houzhang Fang, Auteur Année de publication : 2020 Article en page(s) : pp 1777 - 1789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse bivariée
[Termes IGN] analyse comparative
[Termes IGN] filtrage du bruit
[Termes IGN] image infrarouge
[Termes IGN] intensité lumineuse
[Termes IGN] interpolation polynomiale
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] programmation par contraintes
[Termes IGN] texture d'imageRésumé : (Auteur) Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority. Numéro de notice : A2020-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948601 Date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948601 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94663
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1777 - 1789[article]Generalized tensor regression for hyperspectral image classification / Jianjun Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)
[article]
Titre : Generalized tensor regression for hyperspectral image classification Type de document : Article/Communication Auteurs : Jianjun Liu, Auteur ; Zebin Wu, Auteur ; Liang Xiao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1244 - 1258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande spectrale
[Termes IGN] calcul tensoriel
[Termes IGN] classification dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] régression
[Termes IGN] spectromètre imageur
[Termes IGN] tenseurRésumé : (auteur) In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. Numéro de notice : A2020-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2944989 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2944989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94670
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1244 - 1258[article]
Titre : Remote Sensing Applications for Agriculture and Crop Modelling Type de document : Monographie Auteurs : Piero Toscano, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 310 p. ISBN/ISSN/EAN : ISBN 978-3-03928-227-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte d'utilisation du sol
[Termes IGN] changement climatique
[Termes IGN] changement d'occupation du sol
[Termes IGN] engrais chimique
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surface cultivéeRésumé : (éditeur) Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice. Numéro de notice : 25747 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif En ligne : https://www.mdpi.com/books/pdfview/book/2023 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94932
Titre : Remotely sensing the species of individual trees Type de document : Thèse/HDR Auteurs : Yifang Shi, Auteur ; Andrew K. Skidmore, Directeur de thèse ; Tiejun Wang, Directeur de thèse Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2020 Collection : ITC Dissertation num. 376 Importance : 163 p. Format : 21 x 30 cm Note générale : bibliographie
Doctor of Philosophy, Faculty of Geo-Information Science and Earth Observation, University of TwenteLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Abies alba
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] chlorophylle
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tempérée
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge couleur
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Leaf Mass per Area
[Termes IGN] orthoimageRésumé : (auteur) The accurate identification of tree species is critical for the management of forest ecosystems. Mapping of tree species is an important task as it can assist a wide range of environmental applications, such as biodiversity monitoring, ecosystem services assessment, invasive species detection, and sustainable forest management. Compared to the conventional approaches based on labor-intensive field measurements, remote sensing has supplied a large variety of cutting-edge techniques to accomplish forest inventory. However, individual tree species classification in natural mixed forests, as it is typical in central Europe, is still a challenging task. High spectral and structural intra-species variability and inter-species similarity, due to phenological effects, differences in tree age and openness of canopies, shadowing effects, and environment variability, restrict tree species separability. An in-depth understanding of the relationship between species-specific features and remote sensing observations for tree species classification needs further investigation. This thesis aimed to accurately map the species of individual trees using multi-source remotely sensed data, including aerial photographs, airborne LiDAR and hyperspectral data. The research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airborne LiDAR data under leaf-on and leaf-off conditions for individual tree species discrimination. The results empathized the importance of intensity-related LiDAR metrics for tree species identification under both leaf-on and leaf-off conditions. Then, the thesis examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping. The results showed that the texture features generated from multi-temporal digital CIR orthophotos under different view-illumination conditions are species-specific. Combining these texture features with LiDAR metrics significantly improved the accuracy of individual tree species mapping. To explore more valuable species-specific features, the thesis consequently integrated three plant functional traits (i.e. equivalent water thickness, leaf mass per area and leaf chlorophyll) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species. Three selected plant functional traits were accurately retrieved using radiative transfer model and further improved the accuracy of tree species classification. Eventually, the thesis focused on an important tree species silver fir, and accurately mapped individuals of this species based on one-class classifiers using integrated airborne hyperspectral and LiDAR data. The mapping results provided the references locating the areas with a high occurrence probability of silver fir trees and hence increase the efficiency in subsequent field campaigns for forest management and biodiversity monitoring. This thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which enriches the knowledge of species-specific characteristics and related remotely sensed signatures. The emerging of UAVs and the upcoming hyperspectral missions such as EnMAP and HySPIRI deliver valuable datasets with multi-scale coverage and revisit observations, which can be used for mapping the diversity of tree species at stand or regional level. Note de contenu : - General introduction
- Important LiDAR metrics for discriminating tree species
- Improving LiDAR-based tree species mapping using multi-temporal CIR orthophotos
- Tree species classification using remotely sensed plant functional traits
- Mapping individual silver fir trees in a Norway spruce dominated forest
- Synthesis: Mapping individual tree species using multi-source remotely sensed dataNuméro de notice : 17671 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : PhD thesis : : University of Twente : 2020 DOI : 10.3990/1.978903654953-0 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.3990/1.978903654953-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97985 Using remote sensing to assess the effect of time of day on the spatial and temporal variation of LST in urban areas / Akram Abdulla (2020)
Titre : Using remote sensing to assess the effect of time of day on the spatial and temporal variation of LST in urban areas Type de document : Thèse/HDR Auteurs : Akram Abdulla, Auteur ; Kevin Tansey, Directeur de thèse ; Kristen Barrett, Directeur de thèse Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2020 Importance : 128 p. Format : 21 x 30 cm Note générale : bibliographie
Thesis submitted for the degree of Doctor of Philosophy at The University of Leicester, School of Geography, Geology and EnvironmentLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données spatiotemporelles
[Termes IGN] ilot thermique urbain
[Termes IGN] image infrarouge
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] occupation du sol
[Termes IGN] phénomène climatique extrême
[Termes IGN] température au sol
[Termes IGN] variation diurne
[Termes IGN] variation saisonnière
[Termes IGN] variation temporelle
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis seeks to add to the study of the relationship between land surface temperature (LST) and urban land cover by presenting a method to project Landsat LST data from the satellite overpass time (9:40 am) to a local peak of temperature (estimated to be around 1:15 pm locally), to investigate the impact of the time of image acquisition on modelling the spatial and temporal variations of LST. Additionally, it would also verify the effects of extreme temperature to reach more representative seasonal images.The study uses remote sensing data extracted from Landsat 5 and 8 (30 m resolution) and the Spinning Enhanced Visible and Infrared Imager LST products (SEVIRI 3 km resolution), in addition to LST-based measurements collected from the ground. The study presented a method to convert Landsat images to be estimated during local peaks in LST with an accuracy of: standard error of 1.7°C and an R of 0.82 in comparison with actual ground-based measurements. This allowed an investigation of the effects of time of day on the spatial and temporal variation of LST, where it was found that this factor has clearly affected the relationship between LST and urban land cover. Similarly, the time of day has caused differences in estimating LST change over several years. It is also found that the extreme values of temperature can affect the trend of LST temporal variation, and which can be minimized by using the images in the form of the average of seasonal images for each year rather than images being used in a standalone manner. This study contributes to the improved study of LST by minimizing the uncertainty that can occur because of the angle of the sun and associated factors such as shadows, which has long been a controversial issue among researches due to the lack of appropriate satellite data. Note de contenu : 1- Introduction
2- Literature review
3- Study area
4- Converting Landsat LST data from morning to peak temperatures(9:40 am to 1:15 pm)
5- Assessing the effect of the time of day on the spatial variation of LST
6- Assessment and enhancement of the temporal variation of LST over a time series
7- General Discussion and ConclusionsNuméro de notice : 28304 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Geography, Geology and Environment : University of Leicester : 2020 DOI : sans En ligne : https://doi.org/10.25392/leicester.data.14518848.v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98068 PermalinkA machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkAlbedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkTree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkPermalinkUnderstanding of atmospheric systems with efficient numerical methods for observation and prediction / Lei-Ming Ma (2019)PermalinkLittoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)PermalinkSuperpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)PermalinkTERRISCOPE, une nouvelle plateforme mutualisée de recherche en télédétection optique à partir d’avions et de drones / Yannick Boucher (2018)PermalinkArea-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds / Paweł Hawryło in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)Permalink