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A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL) / Anchal Kumawat in The Visual Computer, vol 38 n° 11 (November 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 11 (November 2022) . - pp 3681 - 3702 Titre : A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL) Type de document : Article/Communication Auteurs : Anchal Kumawat, Auteur ; Sucheta Panda, Auteur Année de publication : 2022 Article en page(s) : pp 3681 - 3702 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] base de données d'images [Termes IGN] détection de contours [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre de Wiener [Termes IGN] Inférence floue [Termes IGN] logique floue [Termes IGN] méthode robuste [Termes IGN] restauration d'image [Termes IGN] seuillage [Termes IGN] superposition d'images Résumé : (auteur) The problem of edge detection plays a crucial role in almost all research areas of image processing. If edges are detected accurately, one can detect the location of objects and the parameters such as shape and area can be measured more precisely. In order to overcome the above problem, a feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges. The major contributions of the present work are summarized in three steps. In the first step, a restoration-based enhancement algorithm is proposed to get a fine image from a distorted noisy image. In the second step, two versions of input images are registered using a modified FBIR approach. In the third step, to overcome the drawback of canny edge detection algorithm, each step of the algorithm is modified. The output is then fed to a “fuzzy inference system”. The “fuzzy rule-based technique”, when applied to the problem of “edge detection”, is very “efficient” because the thickness of the edges can be controlled by simply changing “rules and output parameters”. The domain of the images under consideration is various well-known image databases such as Berkeley and USC-SIPI databases, whereas the proposed method is also suitable for other types of both indoor and outdoor images. The robustness of the proposed method is analysed, compared and evaluated with seven image assessment quality (IAQ) parameters. The performance of the proposed method is compared with some of the state-of-the-art edge detection methods in terms of the seven IAQ parameters. Numéro de notice : A2022-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02196-1 Date de publication en ligne : 14/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02196-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102041 [article]

A general model for creating robust choropleth maps / Wangshu Mu in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101850 Titre : A general model for creating robust choropleth maps Type de document : Article/Communication Auteurs : Wangshu Mu, Auteur ; Daoqin Tong, Auteur Année de publication : 2022 Article en page(s) : n° 101850 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] carte choroplèthe [Termes IGN] incertitude des données [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] méthode robuste [Termes IGN] optimisation par essaim de particules [Termes IGN] programmation dynamique Résumé : (auteur) Choropleth maps visualize areal geographical data by grouping data into a few map classes and assigning different colors, shades, or patterns. Recent studies show that data uncertainty, commonly observed in real-life applications, should also be accounted for when determining the best classification scheme. Due to data uncertainty, a few studies note that map units might be placed in a wrong class, and the concept of map robustness has been introduced to minimize such misplacement. Recently, an algorithm has been developed to integrate robustness into the design of the optimal map classification scheme. However, the existing algorithm has two limitations: first, it is only suitable for certain robustness metrics. Second, when identifying the optimal class breaks, the existing algorithm requires predefined candidate class break values, which might lead to sub-optimal solutions. This paper resolves these issues by proposing a new model, namely, the Continuous Robust Map Classification Problem (CRMCP), and the associated solution approach. The CRMCP allows mapmakers to customize robustness metrics according to their data and applications. In addition, a particle swarm optimization algorithm is developed to solve the CRMCP. The model and algorithm are tested using American Community Survey data. Test results suggest that the new approach can find better solutions than the existing algorithm. The study improves the usability of choropleth maps when uncertain geographical attributes are involved and allows spatial analysts and decision-makers to incorporate robustness into the mapmaking process more flexibly. Numéro de notice : A2022-514 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101850 Date de publication en ligne : 28/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101055 [article]

Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference / Xiao Huang in Transactions in GIS, vol 26 n° 4 (June 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 4 (June 2022) . - pp 1939 - 1961 Titre : Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference Type de document : Article/Communication Auteurs : Xiao Huang, Auteur ; Yang Xu, Auteur ; Rui Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1939 - 1961 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multiéchelle [Termes IGN] disparité [Termes IGN] distribution spatiale [Termes IGN] données socio-économiques [Termes IGN] épidémie [Termes IGN] estimation bayesienne [Termes IGN] hétérogénéité spatiale [Termes IGN] inférence statistique [Termes IGN] logement [Termes IGN] maladie virale [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode robuste Résumé : (auteur) In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian inference with weakly informative priors and by examining how home-dwelling stages in the USA varied geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (9 days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections at U.S. state and county level reveal notable spatial disparity in home-dwelling stage-related variables. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the USA from a purely data-driven perspective and in a statistically robust manner. Numéro de notice : A2022-533 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article DOI : 10.1111/tgis.12918 Date de publication en ligne : 17/03/2022 En ligne : https://doi.org/10.1111/tgis.12918 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101081 [article]

Deformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)  

Public [article]  inGeodetski vestnik > vol 66 n° 1 (March 2022) . - pp 60 - 75 Titre : Deformation analysis: the modified GREDOD method Type de document : Article/Communication Auteurs : Mehmed Batilović, Auteur ; Željko Kanović, Auteur ; Zoran Sušić, Auteur ; Marko Z. Marković, Auteur ; Vladimir Bulatović, Auteur Année de publication : 2022 Article en page(s) : pp 60 - 75 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] algorithme génétique [Termes IGN] déformation géométrique [Termes IGN] méthode robuste [Termes IGN] optimisation par essaim de particules Résumé : (auteur) In this paper, a modified Generalised Robust Estimation of Deformation from Observation Differences (GREDOD) method is presented, based on the application of genetic algorithm (GA) and generalised particle swarm optimisation (GPSO) algorithm in solving the optimisation problem of this method, which is, in essence, a problem of determining the optimal datum of the displacement vector. The procedure of deformation analysis using this modification of the GREDOD method is demonstrated in the example of the two-dimensional geodetic network presented in numerous research and in which all observations and displacements were simulated. Using both algorithms, GA and GPSO, almost identical results of deformation analysis were obtained, except datum solutions of the displacement vector, which are completely different. These results differ only slightly from the results obtained using the methods of Hannover, Karlsruhe, Delft, Fredericton, München, Caspary, and the classical robust method. Numéro de notice : A2022-453 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2022.01.60-75 En ligne : https://dx.doi.org/10.15292/geodetski-vestnik.2022.01.60-75 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100984 [article]

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An open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102663 Titre : An open science and open data approach for the statistically robust estimation of forest disturbance areas Type de document : Article/Communication Auteurs : Saverio Francini, Auteur ; Ronald E. McRoberts, Auteur ; Giovanni d' Amico, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] coupe rase (sylviculture) [Termes IGN] détection de changement [Termes IGN] estimation statistique [Termes IGN] Fagus sylvatica [Termes IGN] Google Earth Engine [Termes IGN] image Sentinel-MSI [Termes IGN] Italie [Termes IGN] méthode robuste [Termes IGN] perturbation écologique [Termes IGN] Quercus cerris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus pubescens [Termes IGN] Quercus sessiliflora [Termes IGN] surveillance forestière Résumé : (auteur) Forest disturbance monitoring is critical for understanding forest-related greenhouse gas emissions and for determining the role of forest management in mitigating climate change. Multiple algorithms for the automated mapping of forest disturbance using remotely sensed imagery have been developed and applied; however, variability in natural and anthropogenic disturbance phenomena, as well as image acquisition conditions, can result in maps that may be incomplete or that contain inaccuracies that prevent their use for directly estimating areas of disturbance. To reduce errors in reporting disturbance areas, stratified estimators can be applied to obtain statistically robust area estimates, while simultaneously circumventing the need to conduct a complete census or in situations where such a census may not be possible. We present a semi-automated procedure for implementation in Google Earth Engine, 3I3D-GEE, for regional to global mapping of forest disturbance (including clear-cut harvesting, fire, and wind damage) and sample-based estimation of related areas using data from the processing capacity of Google Earth Engine. Documentation for the application is also provided in Appendix A. Using Sentinel-2 (S2) imagery, our procedure was applied and tested for 2018 in Italy for which the approximately 11 million ha of forests (mostly Q. pubescens, Q. robur, Q. cerris, Q. petraea, and Fagus sylvatica) serve as an appropriate case study because national statistics on forest disturbance areas are not available. To decrease the overall standard errors of the area estimates, the sampling intensities in areas where greater variability in the form of greater commission and omission errors are expected can be increased. To this end, we augmented the predicted forest disturbance map with a buffer class consisting of a two-pixel buffer (20 m) on each side of the disturbance class boundary. We selected a reference sample of 19,300 points: a simple random sample of 9,300 points from the buffer and simple random samples of 5000 from each of the undisturbed and disturbed classes. The reference sample was photointerpreted using fine resolution orthophotos (30 cm) and S2 imagery. While the estimate of the disturbed area obtained by adding the areas of pixels classified as disturbed was 41,732 ha, the estimate obtained using the unbiased stratified estimator was 27% greater at 57,717716 ha. Regarding map accuracy, we found several omission errors in the buffer (53.4%) but none (0%) in the undisturbed map class. Similarly, among the 1035 commission errors, the majority (7 4 4) were in the buffer class. The methods presented herein provide a useful tool that can be used to estimate areas of forest disturbance, which many nations must report as part of their commitment to international conventions and treaties. In addition, the information generated can support forest management, enabling the forest sector to monitor stand-replacing forest harvesting over space and time. Numéro de notice : A2022-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102663 En ligne : https://doi.org/10.1016/j.jag.2021.102663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99437 [article]

A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)  

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Registration of heterogenous data for urban modeling / Rahima Djahel (2022)  

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Robust approach for urban road surface extraction using mobile laser scanning 3D point clouds / Abdul Nurunnabi (2022)  

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Robust GNSS phase tracking using variational bayesian inference / Fabio Fabozzi (2022)  

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Variations de volume des lacs pour l'analyse climatique / Iris Lucas (2022)   

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Fast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)  

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Feature matching for multi-epoch historical aerial images / Lulin Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)    

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A deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

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Double adaptive intensity-threshold method for uneven Lidar data to extract road markings / Chengming Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

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A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers / N. Orouji in GPS solutions, vol 25 n° 3 (July 2021)  

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