Centre dedocumentation
scientifique

A methodology for producing realistic hill-shading map based on shaded relief map, digital orthophotographic map fusion and IHS transformation / Hongyun Zeng in Annals of GIS, vol 27 n° 4 (October 2021)  

Public [article]  inAnnals of GIS > vol 27 n° 4 (October 2021) . - pp 371 - 382 Titre : A methodology for producing realistic hill-shading map based on shaded relief map, digital orthophotographic map fusion and IHS transformation Type de document : Article/Communication Auteurs : Hongyun Zeng, Auteur ; Zhiqiang Xie, Auteur ; Jinqu Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 371 - 382 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] données vectorielles [Termes IGN] effet d'ombre [Termes IGN] espace colorimétrique [Termes IGN] géomorphologie [Termes IGN] modèle numérique de surface [Termes IGN] ombre [Termes IGN] orthophotocarte [Termes IGN] représentation du relief [Termes IGN] teinte hypsométrique [Termes IGN] transformation intensité-teinte-saturation Résumé : (auteur) The traditional hill-shading map is usually produced from a digital elevation model (DEM) by using the method of hypsometric tinting, which is capable of demonstrating the changes in geomorphology by setting the colors for hill-shading. However, the disadvantage is obvious that the surface features of the terrain can only be utilized by putting vector data on the map. Hence, the terrain display effect will be altered, especially in the production of large-scale maps, for which the artistic effect will be greatly weakened. This paper proposes a solution to this problem. First, we transform the RGB color space of the Digital orthophotographic map (DOM) image into the intensity-hue-saturation (IHS) color space. Then, we calculate the new value of the intensity as I′ for each pixel of the shaded relief model (SRM) of the high-resolution remote sensing image. Finally, we replace the component I with the new component I′ and then proceed with the inverse IHS transform. The case study shows that an objective representation of the actual situation is presented in the mapping area, and the 3D performance capabilities are enhanced. This research indicates that when the method of fusing the processed SRM with the IHS color system is used, the optimum index factor (OIF) and entropy of the generated map are 41.26 and 12.05, respectively, which are much greater than for the results of the traditional method. In other words, the proposed method can greatly enhance the terrain effect. Numéro de notice : A2021-667 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2021.1921026 Date de publication en ligne : 04/05/2021 En ligne : https://doi.org/10.1080/19475683.2021.1921026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98816 [article]

Elevation models for reproducible evaluation of terrain representation / Patrick Kennelly in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 1 (January 2021) . - pp 63 - 77 Titre : Elevation models for reproducible evaluation of terrain representation Type de document : Article/Communication Auteurs : Patrick Kennelly, Auteur ; Tom Patterson, Auteur ; Bernhard Jenny, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 63 - 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] altitude [Termes IGN] données multiéchelles [Termes IGN] figuré du terrain [Termes IGN] modèle numérique de surface [Termes IGN] réalité de terrain [Termes IGN] relief [Termes IGN] représentation du relief [Termes IGN] reproductibilité [Termes IGN] visualisation de données Résumé : (auteur) This paper proposes elevation models to promote, evaluate, and compare various terrain representation techniques. Our goal is to increase the reproducibility of terrain rendering algorithms and techniques across different scales and landscapes. We introduce elevation models of varying terrain types, available to the user at no cost, with minimal common data imperfections such as missing data values, resampling artifacts, and seams. Three multiscale elevation models are available, each consisting of a set of elevation grids, centered on the same geographic location, with increasing cell sizes and spatial extents. We also propose a collection of single-scale elevation models of archetypal landforms including folded ridges, a braided riverbed, active and stabilized sand dunes, and a volcanic caldera. An inventory of 78 publications with a total of 155 renderings illustrating terrain visualization techniques guided the selection of landform types in the elevation models. The benefits of using the proposed elevation models include straightforward comparison of terrain representation methods across different publications and better documentation of the source data, which increases the reproducibility of terrain representations. Numéro de notice : A2021-715 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1830856 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.1080/15230406.2020.1830856 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96459 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Initialization methods of convolutional neural networks for detection of image manipulations / Ivan Castillo Camacho (2021)  

Public Titre : Initialization methods of convolutional neural networks for detection of image manipulations Titre original : Méthodes d'initialisation des réseaux de neurones convolutifs pour la détection des manipulations d'images Type de document : Thèse/HDR Auteurs : Ivan Castillo Camacho, Auteur ; Kai Wang, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 145 p. Format : 21 x 30 cm Note générale : bibliographie Thèse pour obtenir le grade de Docteur de l'Université Grenoble, spécialité : signal, image, paroles, télécoms Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] altération [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] covariance [Termes IGN] détection d'anomalie [Termes IGN] estompage [Termes IGN] filtre passe-haut [Termes IGN] flux de données [Termes IGN] infraction [Termes IGN] manipulation de données [Termes IGN] qualité des données [Termes IGN] retouche [Termes IGN] variance Index. décimale : THESE Thèses et HDR Résumé : (auteur) Fake images and videos have engulfed mass communication media. This is not something recent, manipulations and forgeries have occurred since the advent of photography itself. These alterations can go from innocent retouches in an attempt to make an image visually attractive to the spread of misleading information or even the use of false media in legal instances. Accordingly, the creation of methods that can help us assure the authenticity of an image presented as non-modified is of paramount importance. In this thesis, we aim at detecting image manipulation operations using deep learning techniques. We present three methods showing the progression of our work under one common objective, i.e, the design and test of Convolutional Neural Network (CNN) initialization methods for image forensic problems with a variance stability focus for the output of a CNN layer.First, we carry out an extensive review of the state of the art in deep-learning-based methods for image forensics. From this review we can confirm that the first layer of a CNN has big impact on the final performance. Specifically, the initialization used on the first-layer filters plays an important role that should be in line with the image forensic task in hand.As our first attempt to address this research problem, we propose a low-complexity initialization method for CNNs. Taking advantage of previous methods designed for the computer vision field, we extend the popular Xavier method to design a filter that would provide variance stability after a convolution operation. This method generates a set of random high-pass filters for the initialization of a CNN's first layer. These filters allow us to better identify forensic traces which usually lie towards the high-frequency part of the image.This first approach constitutes a good staring point of our work. However, a wrong assumption, largely utilized in the research community, was made. This is corrected in our second method where we follow a different data-dependent approach and take into consideration the real statistical properties of natural images. Accordingly, we propose a scaling method for first-layer filters which can cope well with different CNN initialization algorithms. The objective remains in keeping the stability of the variance of data flow in a CNN. We also present theoretical and experimental studies on the output variance for convolutional filter, which are the basis of our proposed data-dependent scaling.Next we describe a revisited version of our first proposal now with a corrected assumption on the statistics of natural images. More precisely, we propose an improved random high-pass initialization method which does not explicitly compute the statistics of input data. We believe that such a ``data-independent'' approach has higher flexibility and broader application range than our second method in situations where the computation of input statistics is not possible.Our proposed methods are tested over several image forensic problems and different CNN architectures.Finally, during all this thesis work we took part in a challenge competition of image forgery detection organized by the French National Research Agency and the French Directorate General of Armaments. We explain in the Appendix the objectives of the challenge along with a brief description of our work conducted for the competition. Note de contenu : 1- Introduction 2- Background knowledge and state of the art 3- Random high-pass initialization 4- Data-dependent initialization 5- Revisiting the random high-pass initialization 6- Conclusions and perspectives Numéro de notice : 28437 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : signal, image, paroles, télécoms : Grenoble : 2021 DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03346063/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98833

Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246 Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] base de données altimétriques [Termes IGN] classification floue [Termes IGN] collecte de données [Termes IGN] données localisées des bénévoles [Termes IGN] figuré du terrain [Termes IGN] méthode heuristique [Termes IGN] modèle numérique de surface [Termes IGN] montagne [Termes IGN] OpenStreetMap [Termes IGN] sommet (relief) [Termes IGN] système d'information géographique Résumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 Date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870 [article]

Robust multisource remote sensing image registration method based on scene shape similarity / Ming Hao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 725 - 736 Titre : Robust multisource remote sensing image registration method based on scene shape similarity Type de document : Article/Communication Auteurs : Ming Hao, Auteur ; Jian Jin, Auteur ; Mengchao Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 725 - 736 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] appariement de modèles conceptuels de données [Termes IGN] coefficient de corrélation [Termes IGN] figuré du terrain [Termes IGN] image multibande [Termes IGN] image radar moirée [Termes IGN] niveau de gris (image) [Termes IGN] points homologues [Termes IGN] superposition d'images [Termes IGN] temps de pose [Termes IGN] transformation linéaire Résumé : (Auteur) Image registration is an indispensable component of remote sensing applications, such as disaster monitoring, change detection, and classification. Grayscale differences and geometric distortions often occur among multisource images due to their different imaging mechanisms, thus making it difficult to acquire feature points and match corresponding points. This article proposes a scene shape similarity feature (SSSF) descriptor based on scene shape features and shape context algorithms. A new similarity measure called SSSFncc is then defined by computing the normalized correlation coefficient of the SSSF descriptors between multisource remote sensing images. Furthermore, the tie points between the reference and the sensed image are extracted via a template matching strategy. A global consistency check method is then used to remove the mismatched tie points. Finally, a piecewise linear transform model is selected to rectify the remote sensing image. The proposed SSSFncc aims to extract the scene shape similarity between multisource images. The accuracy of the proposed SSSFncc is evaluated using five pairs of experimental images from optical, synthetic aperture radar, and map data. Registration results demonstrate that the SSSFncc similarity measure is robust enough for complex nonlinear grayscale differences among multisource remote sensing images. The proposed method achieves more reliable registration outcomes compared with other popular methods. Numéro de notice : A2019-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.725 Date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.725 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93989 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019101	SL	Revue	Centre de documentation	Revues en salle	Disponible

Automated Swiss-style relief shading and rock hachuring / Roman Geisthövel in Cartographic journal (the), Vol 55 n° 4 (November 2018)  

Permalink

A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling / Ángel M. Felicísimo in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

Permalink

The transformation of relief representation on topographic maps in Hungary: from hachures to contour lines / Lazlo Zentai in Cartographic journal (the), vol 55 n° 2 (May 2018)  

Permalink

Modeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Permalink

Interactive shearing for terrain visualization : an expert study / Jonas Buddeberg in Geoinformatica [en ligne], vol 21 n° 3 (July - September 2017)  

Permalink

Progressive block graying and landmarks enhancing as intermediate representations between buildings and urban areas / Guillaume Touya (2017) 

Permalink

Hybrid terrain rendering based on the external edge primitive / E.G. Paredes in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

Permalink

Using hachures to construct a 3D doline model automatically / Nai Yang in Cartographica, vol 50 n° 2 (Summer 2015)  

Permalink

Perceptually shaded slope maps for the visualization of digital surface models / Thomas J. Pingel in Cartographica, vol 49 n° 4 (December 2014)  

Permalink

Changing the light azimuth in shaded relief representation by clustering aspect / Fabio Veronesi in Cartographic journal (the), vol 51 n° 4 (November 2014)  

Permalink