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Correction of distorsions in YG-12 high-resolution panchromatic images / Yonghua Jiang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 1 (January 2015)
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Titre : Correction of distorsions in YG-12 high-resolution panchromatic images Type de document : Article/Communication Auteurs : Yonghua Jiang, Auteur ; Guo Zhang, Auteur ; Deren Li, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction géométrique
[Termes IGN] distorsion d'image
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] géométrie de l'image
[Termes IGN] image panchromatique
[Termes IGN] image YG
[Termes IGN] modèle de déformation des images
[Termes IGN] qualité géométrique (image)Résumé : (auteur) Design deficiencies and hardware limitations cause a number of issues with the images acquired by Chinese satellites launched before 2012, such as YG-12. The geometric quality of the images recorded by YG-I2 cannot match its high resolution because of serious time-synchronization errors and interior distortions. To improve the geometric quality of YG-12 images, this paper proposes a method of interior calibration for the YG-12 panchromatic sensor. In addition, an innovative method is proposed to eliminate time-synchronization errors using parallel observations of the panchromatic sensor on board YG-12. The experimental results indicate the interior parameters of the panchromatic sensor are determined with an accuracy of better than 0.32 pixels, and seamless mosaic images can he obtained after the elimination of distortions. Furthermore, the positioning accuracy with relatively few ground control points is shown to be better than 1.5 pixels. Numéro de notice : A2015-016 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.1.25 En ligne : http://www.ingentaconnect.com/content/asprs/pers/2015/00000081/00000001/art00001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75150
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 1 (January 2015) . - pp 25 - 36[article]Data-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)
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Titre : Data-driven feature learning for high resolution urban land-cover classification Type de document : Thèse/HDR Auteurs : Piotr Andrzej Tokarczyk, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2015 Collection : Dissertationen ETH num. 22544 Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse en composantes principales
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] environnement de développement
[Termes IGN] image à très haute résolution
[Termes IGN] image à ultra haute résolution
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] prise en compte du contexte
[Termes IGN] ruissellement
[Termes IGN] surface imperméable
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) Automated classification of aerial and satellite images is one of the fundamental challenges in remote sensing research. Over the last 30 years, researchers have tried to overcome the tedious and time consuming manual interpretation of images. With the advent of digital technologies, classification approaches facilitating image interpretation have emerged. They were quickly embraced, and nowadays classification of remote sensing imagery is a mature field with many well-established methods. However, a major yet largely unsolved problem is the design and selection of features, that would be appropriate for a specific classification task. Usually, it is not known in advance which image features would help separating object classes in an optimal way and manual feature by trial and error is still a common practice. In the last decade rapid development of remote sensing sensors gave the end-user access to very high resolution imagery. At a ground sampling distance below a meter, small objects and ne-grained texture of larger objects emerge. Thus, to properly exploit the information that these images contain, additional contextual and textural properties of objects should be extracted. Unfortunately, classification of such images is often performed using features tailored to low- and medium resolution sensors: raw pixel values, usually augmented with either simple band ratios (e.g. in form of vegetation indices), or specific texture filter banks (e.g. Gabor filters).
In this thesis, we consider the problem of feature design and selection for classification of urban land-cover from very high resolution (VHR) remote sensing images. To appropriately capture characteristic object patterns, we propose a set of simple and efficient features, called random quasi-exhaustive (RQE) feature bank. It consists of a multitude of multiscale texture features computed efficiently via integral images inside a sliding window. At the same time, we propose to sidestep manual feature selection, and let a boosting classifier choose only those features from a RQE feature bank that are able to efficiently discriminate between different object classes in a specific classification task. We believe that the proposed feature set is fairly generic to many urban remote sensing datasets, such that the features selected by the classifier can be adapted to the characteristics of a certain image: different lighting or different scene structures.
We start with presenting the developed framework for supervised classification of land-cover in urban environments. We demonstrate the efficiency of a boosting classifier used in conjunction with the RQE feature databank on five different very high resolution remote sensing datasets. Next, we move from supervised feature learning to unsupervised methods. Using random forest classifier, we investigate the performance of features extracted using data-driven methods, such as principal component analysis (PCA) or Deep Belief Networks (DBN). We show that, at least in our study, complex unsupervised and non-linear feature learning did not improve classification accuracy over standard linear baseline methods. Finally, we use the developed supervised classification framework for an application in the field of urban hydrology. We produce imperviousness maps, which are then used to model rainfall-runoff processes in urban catchments. We show that the proposed method yields results superior over state-of-the-art methods in the field of urban hydrology. Furthermore, we perform an end-to-end comparison, in which different image data sources produced using different classification methods are used as an input for a hydraulic sewer model.Numéro de notice : 17202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Doctoral thesis : Sciences : ETH Zurich : 2015 En ligne : http://dx.doi.org/10.3929/ethz-a-010414770 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81178 Délimitation des parcelles agricoles par classification d'images Pléiades / Nesrine Chehata in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)
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[article]
Titre : Délimitation des parcelles agricoles par classification d'images Pléiades Type de document : Article/Communication Auteurs : Nesrine Chehata , Auteur ; Karim Ghariani, Auteur ; Arnaud Le Bris
, Auteur ; Philippe Lagacherie, Auteur
Année de publication : 2015 Conférence : Pleiades Days 2014 01/04/2014 03/04/2014 Toulouse France Article en page(s) : pp 165 - 171 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Pléiades-HR
[Termes IGN] parcelle agricole
[Termes IGN] prise en compte du contexte
[Termes IGN] segmentation d'imageRésumé : (Auteur) Les pratiques et les arrangements spatiaux des parcelles agricoles ont un fort impact sur les flux d’eau dans les paysages cultivés. Afin de surveiller les paysages à grande échelle, il y a un fort besoin de délimitation automatique ou semi-automatique des parcelles agricoles. Cet article montre la contribution des images satellitaires à très haute résolution spatiales, telles que Pléiades, pour délimiter le parcellaire agricole de manière automatique. Une approche originale utilisant une classification binaire supervisée des limites parcellaires est proposée. Une approche d’apprentissage actif est mise en oeuvre afin d’adapter le modèle de classifieur au contexte local permettant ainsi la délimitation parcellaire à grande échelle. Le classifieur des Forêts Aléatoires est utilisé pour la classification et la sélection des attributs. Le concept de marge non supervisée est utilisé comme mesure d’incertitude dans l’algorithme d’apprentissage actif. En outre, un étiquetage automatique des pixels incertains est proposé en utilisant une approche hybride combinant une approche région et le concept de marge. Des résultats satisfaisants sont obtenus sur une image Pléiades. Différentes stratégies d’apprentissage sont comparées et discutées. Pour un cas d’étude opérationnel, un modèle global ou bien un modèle simple enrichi peuvent être utilisés en fonction des données de terrain disponibles. Numéro de notice : A2015-083 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2015.220 En ligne : https://doi.org/10.52638/rfpt.2015.220 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75447
in Revue Française de Photogrammétrie et de Télédétection > n° 209 (Janvier 2015) . - pp 165 - 171[article]Documents numériques
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delimitation parcelles agricolesAdobe Acrobat PDFDepth, anisotropy, and water equivalent of snow estimated by radar interferometry and polarimetry / Silvan Leinss (2015)
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Titre : Depth, anisotropy, and water equivalent of snow estimated by radar interferometry and polarimetry Type de document : Thèse/HDR Auteurs : Silvan Leinss, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2015 Collection : Dissertationen ETH num. 23093 Importance : 243 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] anisotropie
[Termes IGN] image TerraSAR-X
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] MNS TerraSAR & TanDEM-X
[Termes IGN] neige
[Termes IGN] polarimétrie radarRésumé : (auteur) Snow contributes to the water supply of almost one-sixth of the world's population and has a strong influence on the energy balance of the earth. Snow provides water for life but also threatens life in the form of avalanches and flooding due to snow melt. Most of the world's snow cover is located in remote and inaccessible regions, therefore large-scale snow monitoring is only possible with remote sensing techniques. In the entire electromagnetic spectrum, ranging from kilometer long radio waves to ultrashort gamma waves, only three atmospheric spectral windows exit through which satellites can observe the surface of the earth. Two of them, the optical and the infrared window, are often blocked by clouds or atmospheric water vapor. Visible or infrared light, which is reflected at the snow surface, is difficult to be used for derivation of any volumetric information of the snow pack. Active and passive microwave systems, which operate in the radio window, have the potential to obtain volumetric information of snow because microwaves can penetrate the snow cover. The aim of this thesis is to determine snow properties, like snow depth, snow anisotropy, and snow water equivalent, by analyzing phase differences of radar signals reflected from snow covered regions. Current radar systems provide not only the backscatter intensity of an object, but also an object-specific scattering phase. The phase contains information about object properties as well as accurate information about the propagation delay time. In this thesis, phase differences resulting from propagation delays are analyzed with respect to different polarizations, observation times and observation geometries. Based on polarimetric phase differences, a method to determine the depth of fresh snow was developed. The copolar phase difference (CPD) obtained from radar images acquired with vertically and horizontally polarized microwaves by the satellites TerraSAR-X and TanDEM-X were analyzed. Positive phase differences could be explained by a horizontal anisotropy in fresh snow, which results from snow settling. As the phase difference is a volumetric property, the magnitude of the phase difference is roughly proportional to the depth of fresh snow. The validation with snow depth measurements on the ground show that the spatial variability of the depth of fresh snow can be determined with a resolution below 100 m with space-borne sensors like TerraSAR-X. Cold temperatures have been found to decrease observed phase differences due to temperature gradient metamorphism. The observed relation between the CPD and fresh snow, snow settling, and temperature gradient metamorphism provides a contact-less and destruction-free tool to observe the anisotropy, which is a metamorphic state of snow. The measurable dielectric anisotropy is directly linked to the structural anisotropy of snow which is responsible for the mechanical stability as well as the thermal conductivity of the snow pack. This makes the anisotropy relevant for the energy balance of snow and snow covered soil. In order to measure the anisotropy, a rigorous electromagnetic model was developed which provides a parameter free link between three-dimensional two-point correlation functions of the microstructure of snow, the effective permittivity tensor, and the macroscopically measured copolar phase difference. For verification of the model, four years of ground-based radar data, acquired by the SnowScat instrument in Sodankylä, Finland, were analyzed with respect to the frequency and incidence angle dependence of the copolar phase. Computer tomography data were used for validation of the anisotropy determined from the copolar phase difference measured by SnowScat. The unique dataset of the currently longest time series of anisotropy measurements provides a new basis for improvement of existing snow models. Four years of anisotropy data were used to develop and validate a thermodynamic snow model based on meteorological input data. The model consists of three terms which describe snow settling, temperature gradient metamorphism, and relaxation based on isotropic water vapor transport. The model was calibrated by balancing the three terms in order to reproduce the measured anisotropy time series. The results of the model, vertically resolved anisotropy pro les of the snow pack, were validated with anisotropy pro les determined by computer tomography. In comparison to the anisotropy, which determines specific properties of the snow volume, the snow water equivalent (SWE) determines how much water is stored in the snow pack. Differential interferometry, where the phase difference of two radar acquisitions separated by a certain time is analyzed, is a promising tool to determine SWE. However, temporal decorrelation of the phase signal is a major drawback of this technique. A decorrelation time of a few days has been observed in space-borne acquisitions from TerraSAR-X which prevents any successful SWE determination. However, using SnowScat as a ground based radar interferometer, it was possible for the first time to measure the accumulation of SWE during four entire winter seasons. A multi-frequency phase unwrapping technique was used for reconstruction of phase wraps which occurred due to intense snow precipitation. The study was performed at exceptionally high frequencies in the X- and Ku-band and with a very high temporal resolution of only 4 hours. The successful demonstration of differential interferometry to determine SWE raises hope to apply the demonstrated technique on data of future radar satellites which operate at longer repeat times of a few days and lower frequencies of a few GHz. Both methods, the CPD analysis as well as differential interferometry, cannot be vi applied for wet snow. Microwave penetration into wet snow is generally small and most of the reflected energy results from scattering at the snow surface. This is interesting for single-pass SAR interferometry, where phase differences are compared, which are measured by two SAR-sensors which simultaneously observe the same scene with slightly different angles. Single-pass SAR interferometry can provide accurate surface models at a horizontal resolution of a few meters. The difference between two digital elevation models (DEM), one obtained during snow free conditions and one obtained during the onset of snow melt, can therefore provide direct information about snow depth. DEM differencing was applied on TanDEM-X acquisitions from spring and autumn and snow depths maps were obtained which agree with the snow- depth-maps provided by the Institute for Snow and Avalanche Research, SLF. A key requirement for successful snow depth estimation is that the snow surface can be recognized as wet. As the backscatter intensity decreases significantly during snow melt, wet snow detection is straight forward and the total accumulated snow depth of wet spring snow can be determined. This thesis shows that the analysis of the phase signal contained in radar acquisitions provides a broad spectrum of information about the snow pack. The developed method for anisotropy determination provides not only a unique opportunity to improve snow models, but also a method to globally sense the metamorphic state of snow. The currently longest radar-derived time series of SWE measurements raise hope to apply differential interferometry for global SWE determination of dry snow. The shown accuracy for snow depth determination from high frequency, interferometric, single-pass SAR systems demonstrates that such systems are important missions for monitoring changes in snow depth and ice thickness in remote alpine and polar regions in order monitor changes of the global distribution of fresh water stored in the form of ice or snow. Numéro de notice : 17199 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : doctoral thesis : Sciences : ETH Zurich : 2015 En ligne : http://dx.doi.org/10.3929/ethz-a-010603517 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81170
Titre : Essential Earth imaging for GIS Type de document : Guide/Manuel Auteurs : Lawrence Fox III, Auteur Editeur : Redlands [Californie - Etats-Unis] : ESRI Press [Environmental Systems Research Institute] Année de publication : 2015 Importance : 115 p. Format : 19 x 23 cm ISBN/ISSN/EAN : 978-1-58948-345-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] bruit atmosphérique
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] observation de la Terre
[Termes IGN] qualité d'image
[Termes IGN] semis de points
[Termes IGN] système d'information géographiqueIndex. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) A guide to imaging technology and management, Essential Earth Imaging for GIS discusses characteristics of images obtained from aircraft and spacecraft, and how to enhance, register, and visually interpret multispectral imagery and point clouds. Geographic information system (GIS) professionals can use this book to learn about basic imaging technology. Students can use this book as a reference for introductory GIS courses that include multispectral image display and analysis. Companion exercises and access to a free 180-day trial of ArcGIS are available on the Esri Press "Book Resources" webpage, esripress.esri.com/bookresources. Note de contenu :
Introduction
Chapter 1. Overview of imaging GIS
Chapter 2. The physical basis and general methods of remote sensing
Chapter 3. Effects of the atmosphere on image quality
Chapter 4. Creating two-dimensional images with sensors
Chapter 5. Displaying digital images with GIS software
Chapter 6. Generating three-dimensional data with photogrammetric measurements and active sensors
Chapter 7. Image processing
Chapter 8. Extracting information from imagesNuméro de notice : 22424 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79640 Réservation
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